2000 character limit reached
Open-TI: Open Traffic Intelligence with Augmented Language Model (2401.00211v2)
Published 30 Dec 2023 in cs.AI
Abstract: Transportation has greatly benefited the cities' development in the modern civilization process. Intelligent transportation, leveraging advanced computer algorithms, could further increase people's daily commuting efficiency. However, intelligent transportation, as a cross-discipline, often requires practitioners to comprehend complicated algorithms and obscure neural networks, bringing a challenge for the advanced techniques to be trusted and deployed in practical industries. Recognizing the expressiveness of the pre-trained LLMs, especially the potential of being augmented with abilities to understand and execute intricate commands, we introduce Open-TI. Serving as a bridge to mitigate the industry-academic gap, Open-TI is an innovative model targeting the goal of Turing Indistinguishable Traffic Intelligence, it is augmented with the capability to harness external traffic analysis packages based on existing conversations. Marking its distinction, Open-TI is the first method capable of conducting exhaustive traffic analysis from scratch - spanning from map data acquisition to the eventual execution in complex simulations. Besides, Open-TI is able to conduct task-specific embodiment like training and adapting the traffic signal control policies (TSC), explore demand optimizations, etc. Furthermore, we explored the viability of LLMs directly serving as control agents, by understanding the expected intentions from Open-TI, we designed an agent-to-agent communication mode to support Open-TI conveying messages to ChatZero (control agent), and then the control agent would choose from the action space to proceed the execution. We eventually provide the formal implementation structure, and the open-ended design invites further community-driven enhancements.
- Chao, Q., Bi, H., Li, W., Mao, T., Wang, Z., Lin, M.C., Deng, Z.: A survey on visual traffic simulation: Models, evaluations, and applications in autonomous driving. In: Computer Graphics Forum, vol. 39, pp. 287–308 (2020). Wiley Online Library (3) Dai, Z., Liu, X.C., Chen, X., Ma, X.: Joint optimization of scheduling and capacity for mixed traffic with autonomous and human-driven buses: A dynamic programming approach. Transportation Research Part C: Emerging Technologies 114, 598–619 (2020) (4) Zhou, X.S., Cheng, Q., Wu, X., Li, P., Belezamo, B., Lu, J., Abbasi, M.: A meso-to-macro cross-resolution performance approach for connecting polynomial arrival queue model to volume-delay function with inflow demand-to-capacity ratio. Multimodal Transportation 1(2), 100017 (2022) (5) Wei, H., Xu, N., Zhang, H., Zheng, G., Zang, X., Chen, C., Zhang, W., Zhu, Y., Xu, K., Li, Z.: Colight: Learning network-level cooperation for traffic signal control. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 1913–1922 (2019) (6) Osorio, C.: High-dimensional offline origin-destination (od) demand calibration for stochastic traffic simulators of large-scale road networks. Transportation Research Part B: Methodological 124, 18–43 (2019) (7) Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 2575–2582 (2018). IEEE (8) Zhang, H., Feng, S., Liu, C., Ding, Y., Zhu, Y., Zhou, Z., Zhang, W., Yu, Y., Jin, H., Li, Z.: Cityflow: A multi-agent reinforcement learning environment for large scale city traffic scenario. In: The World Wide Web Conference, pp. 3620–3624 (2019) (9) Fellendorf, M., Vortisch, P.: Microscopic traffic flow simulator vissim. Fundamentals of traffic simulation, 63–93 (2010) (10) Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Dai, Z., Liu, X.C., Chen, X., Ma, X.: Joint optimization of scheduling and capacity for mixed traffic with autonomous and human-driven buses: A dynamic programming approach. Transportation Research Part C: Emerging Technologies 114, 598–619 (2020) (4) Zhou, X.S., Cheng, Q., Wu, X., Li, P., Belezamo, B., Lu, J., Abbasi, M.: A meso-to-macro cross-resolution performance approach for connecting polynomial arrival queue model to volume-delay function with inflow demand-to-capacity ratio. Multimodal Transportation 1(2), 100017 (2022) (5) Wei, H., Xu, N., Zhang, H., Zheng, G., Zang, X., Chen, C., Zhang, W., Zhu, Y., Xu, K., Li, Z.: Colight: Learning network-level cooperation for traffic signal control. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 1913–1922 (2019) (6) Osorio, C.: High-dimensional offline origin-destination (od) demand calibration for stochastic traffic simulators of large-scale road networks. Transportation Research Part B: Methodological 124, 18–43 (2019) (7) Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 2575–2582 (2018). IEEE (8) Zhang, H., Feng, S., Liu, C., Ding, Y., Zhu, Y., Zhou, Z., Zhang, W., Yu, Y., Jin, H., Li, Z.: Cityflow: A multi-agent reinforcement learning environment for large scale city traffic scenario. In: The World Wide Web Conference, pp. 3620–3624 (2019) (9) Fellendorf, M., Vortisch, P.: Microscopic traffic flow simulator vissim. Fundamentals of traffic simulation, 63–93 (2010) (10) Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X.S., Cheng, Q., Wu, X., Li, P., Belezamo, B., Lu, J., Abbasi, M.: A meso-to-macro cross-resolution performance approach for connecting polynomial arrival queue model to volume-delay function with inflow demand-to-capacity ratio. Multimodal Transportation 1(2), 100017 (2022) (5) Wei, H., Xu, N., Zhang, H., Zheng, G., Zang, X., Chen, C., Zhang, W., Zhu, Y., Xu, K., Li, Z.: Colight: Learning network-level cooperation for traffic signal control. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 1913–1922 (2019) (6) Osorio, C.: High-dimensional offline origin-destination (od) demand calibration for stochastic traffic simulators of large-scale road networks. Transportation Research Part B: Methodological 124, 18–43 (2019) (7) Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 2575–2582 (2018). IEEE (8) Zhang, H., Feng, S., Liu, C., Ding, Y., Zhu, Y., Zhou, Z., Zhang, W., Yu, Y., Jin, H., Li, Z.: Cityflow: A multi-agent reinforcement learning environment for large scale city traffic scenario. In: The World Wide Web Conference, pp. 3620–3624 (2019) (9) Fellendorf, M., Vortisch, P.: Microscopic traffic flow simulator vissim. Fundamentals of traffic simulation, 63–93 (2010) (10) Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Xu, N., Zhang, H., Zheng, G., Zang, X., Chen, C., Zhang, W., Zhu, Y., Xu, K., Li, Z.: Colight: Learning network-level cooperation for traffic signal control. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 1913–1922 (2019) (6) Osorio, C.: High-dimensional offline origin-destination (od) demand calibration for stochastic traffic simulators of large-scale road networks. Transportation Research Part B: Methodological 124, 18–43 (2019) (7) Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 2575–2582 (2018). IEEE (8) Zhang, H., Feng, S., Liu, C., Ding, Y., Zhu, Y., Zhou, Z., Zhang, W., Yu, Y., Jin, H., Li, Z.: Cityflow: A multi-agent reinforcement learning environment for large scale city traffic scenario. In: The World Wide Web Conference, pp. 3620–3624 (2019) (9) Fellendorf, M., Vortisch, P.: Microscopic traffic flow simulator vissim. Fundamentals of traffic simulation, 63–93 (2010) (10) Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Osorio, C.: High-dimensional offline origin-destination (od) demand calibration for stochastic traffic simulators of large-scale road networks. Transportation Research Part B: Methodological 124, 18–43 (2019) (7) Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 2575–2582 (2018). IEEE (8) Zhang, H., Feng, S., Liu, C., Ding, Y., Zhu, Y., Zhou, Z., Zhang, W., Yu, Y., Jin, H., Li, Z.: Cityflow: A multi-agent reinforcement learning environment for large scale city traffic scenario. In: The World Wide Web Conference, pp. 3620–3624 (2019) (9) Fellendorf, M., Vortisch, P.: Microscopic traffic flow simulator vissim. Fundamentals of traffic simulation, 63–93 (2010) (10) Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 2575–2582 (2018). IEEE (8) Zhang, H., Feng, S., Liu, C., Ding, Y., Zhu, Y., Zhou, Z., Zhang, W., Yu, Y., Jin, H., Li, Z.: Cityflow: A multi-agent reinforcement learning environment for large scale city traffic scenario. In: The World Wide Web Conference, pp. 3620–3624 (2019) (9) Fellendorf, M., Vortisch, P.: Microscopic traffic flow simulator vissim. Fundamentals of traffic simulation, 63–93 (2010) (10) Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhang, H., Feng, S., Liu, C., Ding, Y., Zhu, Y., Zhou, Z., Zhang, W., Yu, Y., Jin, H., Li, Z.: Cityflow: A multi-agent reinforcement learning environment for large scale city traffic scenario. In: The World Wide Web Conference, pp. 3620–3624 (2019) (9) Fellendorf, M., Vortisch, P.: Microscopic traffic flow simulator vissim. Fundamentals of traffic simulation, 63–93 (2010) (10) Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fellendorf, M., Vortisch, P.: Microscopic traffic flow simulator vissim. Fundamentals of traffic simulation, 63–93 (2010) (10) Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Dai, Z., Liu, X.C., Chen, X., Ma, X.: Joint optimization of scheduling and capacity for mixed traffic with autonomous and human-driven buses: A dynamic programming approach. Transportation Research Part C: Emerging Technologies 114, 598–619 (2020) (4) Zhou, X.S., Cheng, Q., Wu, X., Li, P., Belezamo, B., Lu, J., Abbasi, M.: A meso-to-macro cross-resolution performance approach for connecting polynomial arrival queue model to volume-delay function with inflow demand-to-capacity ratio. Multimodal Transportation 1(2), 100017 (2022) (5) Wei, H., Xu, N., Zhang, H., Zheng, G., Zang, X., Chen, C., Zhang, W., Zhu, Y., Xu, K., Li, Z.: Colight: Learning network-level cooperation for traffic signal control. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 1913–1922 (2019) (6) Osorio, C.: High-dimensional offline origin-destination (od) demand calibration for stochastic traffic simulators of large-scale road networks. Transportation Research Part B: Methodological 124, 18–43 (2019) (7) Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 2575–2582 (2018). IEEE (8) Zhang, H., Feng, S., Liu, C., Ding, Y., Zhu, Y., Zhou, Z., Zhang, W., Yu, Y., Jin, H., Li, Z.: Cityflow: A multi-agent reinforcement learning environment for large scale city traffic scenario. In: The World Wide Web Conference, pp. 3620–3624 (2019) (9) Fellendorf, M., Vortisch, P.: Microscopic traffic flow simulator vissim. Fundamentals of traffic simulation, 63–93 (2010) (10) Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X.S., Cheng, Q., Wu, X., Li, P., Belezamo, B., Lu, J., Abbasi, M.: A meso-to-macro cross-resolution performance approach for connecting polynomial arrival queue model to volume-delay function with inflow demand-to-capacity ratio. Multimodal Transportation 1(2), 100017 (2022) (5) Wei, H., Xu, N., Zhang, H., Zheng, G., Zang, X., Chen, C., Zhang, W., Zhu, Y., Xu, K., Li, Z.: Colight: Learning network-level cooperation for traffic signal control. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 1913–1922 (2019) (6) Osorio, C.: High-dimensional offline origin-destination (od) demand calibration for stochastic traffic simulators of large-scale road networks. Transportation Research Part B: Methodological 124, 18–43 (2019) (7) Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 2575–2582 (2018). IEEE (8) Zhang, H., Feng, S., Liu, C., Ding, Y., Zhu, Y., Zhou, Z., Zhang, W., Yu, Y., Jin, H., Li, Z.: Cityflow: A multi-agent reinforcement learning environment for large scale city traffic scenario. In: The World Wide Web Conference, pp. 3620–3624 (2019) (9) Fellendorf, M., Vortisch, P.: Microscopic traffic flow simulator vissim. Fundamentals of traffic simulation, 63–93 (2010) (10) Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Xu, N., Zhang, H., Zheng, G., Zang, X., Chen, C., Zhang, W., Zhu, Y., Xu, K., Li, Z.: Colight: Learning network-level cooperation for traffic signal control. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 1913–1922 (2019) (6) Osorio, C.: High-dimensional offline origin-destination (od) demand calibration for stochastic traffic simulators of large-scale road networks. Transportation Research Part B: Methodological 124, 18–43 (2019) (7) Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 2575–2582 (2018). IEEE (8) Zhang, H., Feng, S., Liu, C., Ding, Y., Zhu, Y., Zhou, Z., Zhang, W., Yu, Y., Jin, H., Li, Z.: Cityflow: A multi-agent reinforcement learning environment for large scale city traffic scenario. In: The World Wide Web Conference, pp. 3620–3624 (2019) (9) Fellendorf, M., Vortisch, P.: Microscopic traffic flow simulator vissim. Fundamentals of traffic simulation, 63–93 (2010) (10) Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Osorio, C.: High-dimensional offline origin-destination (od) demand calibration for stochastic traffic simulators of large-scale road networks. Transportation Research Part B: Methodological 124, 18–43 (2019) (7) Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 2575–2582 (2018). IEEE (8) Zhang, H., Feng, S., Liu, C., Ding, Y., Zhu, Y., Zhou, Z., Zhang, W., Yu, Y., Jin, H., Li, Z.: Cityflow: A multi-agent reinforcement learning environment for large scale city traffic scenario. In: The World Wide Web Conference, pp. 3620–3624 (2019) (9) Fellendorf, M., Vortisch, P.: Microscopic traffic flow simulator vissim. Fundamentals of traffic simulation, 63–93 (2010) (10) Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 2575–2582 (2018). IEEE (8) Zhang, H., Feng, S., Liu, C., Ding, Y., Zhu, Y., Zhou, Z., Zhang, W., Yu, Y., Jin, H., Li, Z.: Cityflow: A multi-agent reinforcement learning environment for large scale city traffic scenario. In: The World Wide Web Conference, pp. 3620–3624 (2019) (9) Fellendorf, M., Vortisch, P.: Microscopic traffic flow simulator vissim. Fundamentals of traffic simulation, 63–93 (2010) (10) Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhang, H., Feng, S., Liu, C., Ding, Y., Zhu, Y., Zhou, Z., Zhang, W., Yu, Y., Jin, H., Li, Z.: Cityflow: A multi-agent reinforcement learning environment for large scale city traffic scenario. In: The World Wide Web Conference, pp. 3620–3624 (2019) (9) Fellendorf, M., Vortisch, P.: Microscopic traffic flow simulator vissim. Fundamentals of traffic simulation, 63–93 (2010) (10) Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fellendorf, M., Vortisch, P.: Microscopic traffic flow simulator vissim. Fundamentals of traffic simulation, 63–93 (2010) (10) Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Zhou, X.S., Cheng, Q., Wu, X., Li, P., Belezamo, B., Lu, J., Abbasi, M.: A meso-to-macro cross-resolution performance approach for connecting polynomial arrival queue model to volume-delay function with inflow demand-to-capacity ratio. Multimodal Transportation 1(2), 100017 (2022) (5) Wei, H., Xu, N., Zhang, H., Zheng, G., Zang, X., Chen, C., Zhang, W., Zhu, Y., Xu, K., Li, Z.: Colight: Learning network-level cooperation for traffic signal control. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 1913–1922 (2019) (6) Osorio, C.: High-dimensional offline origin-destination (od) demand calibration for stochastic traffic simulators of large-scale road networks. Transportation Research Part B: Methodological 124, 18–43 (2019) (7) Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 2575–2582 (2018). IEEE (8) Zhang, H., Feng, S., Liu, C., Ding, Y., Zhu, Y., Zhou, Z., Zhang, W., Yu, Y., Jin, H., Li, Z.: Cityflow: A multi-agent reinforcement learning environment for large scale city traffic scenario. In: The World Wide Web Conference, pp. 3620–3624 (2019) (9) Fellendorf, M., Vortisch, P.: Microscopic traffic flow simulator vissim. Fundamentals of traffic simulation, 63–93 (2010) (10) Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Xu, N., Zhang, H., Zheng, G., Zang, X., Chen, C., Zhang, W., Zhu, Y., Xu, K., Li, Z.: Colight: Learning network-level cooperation for traffic signal control. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 1913–1922 (2019) (6) Osorio, C.: High-dimensional offline origin-destination (od) demand calibration for stochastic traffic simulators of large-scale road networks. Transportation Research Part B: Methodological 124, 18–43 (2019) (7) Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 2575–2582 (2018). IEEE (8) Zhang, H., Feng, S., Liu, C., Ding, Y., Zhu, Y., Zhou, Z., Zhang, W., Yu, Y., Jin, H., Li, Z.: Cityflow: A multi-agent reinforcement learning environment for large scale city traffic scenario. In: The World Wide Web Conference, pp. 3620–3624 (2019) (9) Fellendorf, M., Vortisch, P.: Microscopic traffic flow simulator vissim. Fundamentals of traffic simulation, 63–93 (2010) (10) Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Osorio, C.: High-dimensional offline origin-destination (od) demand calibration for stochastic traffic simulators of large-scale road networks. Transportation Research Part B: Methodological 124, 18–43 (2019) (7) Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 2575–2582 (2018). IEEE (8) Zhang, H., Feng, S., Liu, C., Ding, Y., Zhu, Y., Zhou, Z., Zhang, W., Yu, Y., Jin, H., Li, Z.: Cityflow: A multi-agent reinforcement learning environment for large scale city traffic scenario. In: The World Wide Web Conference, pp. 3620–3624 (2019) (9) Fellendorf, M., Vortisch, P.: Microscopic traffic flow simulator vissim. Fundamentals of traffic simulation, 63–93 (2010) (10) Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 2575–2582 (2018). IEEE (8) Zhang, H., Feng, S., Liu, C., Ding, Y., Zhu, Y., Zhou, Z., Zhang, W., Yu, Y., Jin, H., Li, Z.: Cityflow: A multi-agent reinforcement learning environment for large scale city traffic scenario. In: The World Wide Web Conference, pp. 3620–3624 (2019) (9) Fellendorf, M., Vortisch, P.: Microscopic traffic flow simulator vissim. Fundamentals of traffic simulation, 63–93 (2010) (10) Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhang, H., Feng, S., Liu, C., Ding, Y., Zhu, Y., Zhou, Z., Zhang, W., Yu, Y., Jin, H., Li, Z.: Cityflow: A multi-agent reinforcement learning environment for large scale city traffic scenario. In: The World Wide Web Conference, pp. 3620–3624 (2019) (9) Fellendorf, M., Vortisch, P.: Microscopic traffic flow simulator vissim. Fundamentals of traffic simulation, 63–93 (2010) (10) Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fellendorf, M., Vortisch, P.: Microscopic traffic flow simulator vissim. Fundamentals of traffic simulation, 63–93 (2010) (10) Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Wei, H., Xu, N., Zhang, H., Zheng, G., Zang, X., Chen, C., Zhang, W., Zhu, Y., Xu, K., Li, Z.: Colight: Learning network-level cooperation for traffic signal control. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 1913–1922 (2019) (6) Osorio, C.: High-dimensional offline origin-destination (od) demand calibration for stochastic traffic simulators of large-scale road networks. Transportation Research Part B: Methodological 124, 18–43 (2019) (7) Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 2575–2582 (2018). IEEE (8) Zhang, H., Feng, S., Liu, C., Ding, Y., Zhu, Y., Zhou, Z., Zhang, W., Yu, Y., Jin, H., Li, Z.: Cityflow: A multi-agent reinforcement learning environment for large scale city traffic scenario. In: The World Wide Web Conference, pp. 3620–3624 (2019) (9) Fellendorf, M., Vortisch, P.: Microscopic traffic flow simulator vissim. Fundamentals of traffic simulation, 63–93 (2010) (10) Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Osorio, C.: High-dimensional offline origin-destination (od) demand calibration for stochastic traffic simulators of large-scale road networks. Transportation Research Part B: Methodological 124, 18–43 (2019) (7) Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 2575–2582 (2018). IEEE (8) Zhang, H., Feng, S., Liu, C., Ding, Y., Zhu, Y., Zhou, Z., Zhang, W., Yu, Y., Jin, H., Li, Z.: Cityflow: A multi-agent reinforcement learning environment for large scale city traffic scenario. In: The World Wide Web Conference, pp. 3620–3624 (2019) (9) Fellendorf, M., Vortisch, P.: Microscopic traffic flow simulator vissim. Fundamentals of traffic simulation, 63–93 (2010) (10) Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 2575–2582 (2018). IEEE (8) Zhang, H., Feng, S., Liu, C., Ding, Y., Zhu, Y., Zhou, Z., Zhang, W., Yu, Y., Jin, H., Li, Z.: Cityflow: A multi-agent reinforcement learning environment for large scale city traffic scenario. In: The World Wide Web Conference, pp. 3620–3624 (2019) (9) Fellendorf, M., Vortisch, P.: Microscopic traffic flow simulator vissim. Fundamentals of traffic simulation, 63–93 (2010) (10) Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhang, H., Feng, S., Liu, C., Ding, Y., Zhu, Y., Zhou, Z., Zhang, W., Yu, Y., Jin, H., Li, Z.: Cityflow: A multi-agent reinforcement learning environment for large scale city traffic scenario. In: The World Wide Web Conference, pp. 3620–3624 (2019) (9) Fellendorf, M., Vortisch, P.: Microscopic traffic flow simulator vissim. Fundamentals of traffic simulation, 63–93 (2010) (10) Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fellendorf, M., Vortisch, P.: Microscopic traffic flow simulator vissim. Fundamentals of traffic simulation, 63–93 (2010) (10) Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Osorio, C.: High-dimensional offline origin-destination (od) demand calibration for stochastic traffic simulators of large-scale road networks. Transportation Research Part B: Methodological 124, 18–43 (2019) (7) Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 2575–2582 (2018). IEEE (8) Zhang, H., Feng, S., Liu, C., Ding, Y., Zhu, Y., Zhou, Z., Zhang, W., Yu, Y., Jin, H., Li, Z.: Cityflow: A multi-agent reinforcement learning environment for large scale city traffic scenario. In: The World Wide Web Conference, pp. 3620–3624 (2019) (9) Fellendorf, M., Vortisch, P.: Microscopic traffic flow simulator vissim. Fundamentals of traffic simulation, 63–93 (2010) (10) Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 2575–2582 (2018). IEEE (8) Zhang, H., Feng, S., Liu, C., Ding, Y., Zhu, Y., Zhou, Z., Zhang, W., Yu, Y., Jin, H., Li, Z.: Cityflow: A multi-agent reinforcement learning environment for large scale city traffic scenario. In: The World Wide Web Conference, pp. 3620–3624 (2019) (9) Fellendorf, M., Vortisch, P.: Microscopic traffic flow simulator vissim. Fundamentals of traffic simulation, 63–93 (2010) (10) Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhang, H., Feng, S., Liu, C., Ding, Y., Zhu, Y., Zhou, Z., Zhang, W., Yu, Y., Jin, H., Li, Z.: Cityflow: A multi-agent reinforcement learning environment for large scale city traffic scenario. In: The World Wide Web Conference, pp. 3620–3624 (2019) (9) Fellendorf, M., Vortisch, P.: Microscopic traffic flow simulator vissim. Fundamentals of traffic simulation, 63–93 (2010) (10) Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fellendorf, M., Vortisch, P.: Microscopic traffic flow simulator vissim. Fundamentals of traffic simulation, 63–93 (2010) (10) Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 2575–2582 (2018). IEEE (8) Zhang, H., Feng, S., Liu, C., Ding, Y., Zhu, Y., Zhou, Z., Zhang, W., Yu, Y., Jin, H., Li, Z.: Cityflow: A multi-agent reinforcement learning environment for large scale city traffic scenario. In: The World Wide Web Conference, pp. 3620–3624 (2019) (9) Fellendorf, M., Vortisch, P.: Microscopic traffic flow simulator vissim. Fundamentals of traffic simulation, 63–93 (2010) (10) Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhang, H., Feng, S., Liu, C., Ding, Y., Zhu, Y., Zhou, Z., Zhang, W., Yu, Y., Jin, H., Li, Z.: Cityflow: A multi-agent reinforcement learning environment for large scale city traffic scenario. In: The World Wide Web Conference, pp. 3620–3624 (2019) (9) Fellendorf, M., Vortisch, P.: Microscopic traffic flow simulator vissim. Fundamentals of traffic simulation, 63–93 (2010) (10) Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fellendorf, M., Vortisch, P.: Microscopic traffic flow simulator vissim. Fundamentals of traffic simulation, 63–93 (2010) (10) Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Zhang, H., Feng, S., Liu, C., Ding, Y., Zhu, Y., Zhou, Z., Zhang, W., Yu, Y., Jin, H., Li, Z.: Cityflow: A multi-agent reinforcement learning environment for large scale city traffic scenario. In: The World Wide Web Conference, pp. 3620–3624 (2019) (9) Fellendorf, M., Vortisch, P.: Microscopic traffic flow simulator vissim. Fundamentals of traffic simulation, 63–93 (2010) (10) Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fellendorf, M., Vortisch, P.: Microscopic traffic flow simulator vissim. Fundamentals of traffic simulation, 63–93 (2010) (10) Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Fellendorf, M., Vortisch, P.: Microscopic traffic flow simulator vissim. Fundamentals of traffic simulation, 63–93 (2010) (10) Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Lu, J., Zhou, X.S.: Virtual track networks: A hierarchical modeling framework and open-source tools for simplified and efficient connected and automated mobility (cam) system design based on general modeling network specification (gmns). Transportation Research Part C: Emerging Technologies 153, 104223 (2023) (11) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Zhang, S., Fu, D., Zhang, Z., Yu, B., Cai, P.: Trafficgpt: Viewing, processing and interacting with traffic foundation models. arXiv preprint arXiv:2309.06719 (2023) (12) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- de Zarzà, I., de Curtò, J., Roig, G., Calafate, C.T.: Llm multimodal traffic accident forecasting. Sensors 23(22), 9225 (2023) (13) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Vaithilingam, P., Zhang, T., Glassman, E.L.: Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp. 1–7 (2022) (14) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Li, Y., Gao, C., Song, X., Wang, X., Xu, Y., Han, S.: Druggpt: A gpt-based strategy for designing potential ligands targeting specific proteins. bioRxiv, 2023–06 (2023) (15) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., Huang, C.: Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023 (2023) (16) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Mialon, G., Dessì, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rozière, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., et al.: Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023) (17) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018) (18) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33, 1877–1901 (2020) (19) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24(240), 1–113 (2023) (20) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) (21) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al.: Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 100017 (2023) (22) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35, 38274–38290 (2022) (23) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al.: Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625 (2022) (24) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Da, L., Gao, M., Mei, H., Wei, H.: Llm powered sim-to-real transfer for traffic signal control. arXiv preprint arXiv:2308.14284 (2023) (25) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Li, M., Song, F., Yu, B., Yu, H., Li, Z., Huang, F., Li, Y.: Api-bank: A benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244 (2023) (26) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Wang, Y., Ma, X., Chen, W.: Augmenting black-box llms with medical textbooks for clinical question answering. arXiv preprint arXiv:2309.02233 (2023) (27) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S., et al.: Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434 (2023) (28) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Tong, L., Pan, Y., Shang, P., Guo, J., Xian, K., Zhou, X.: Open-source public transportation mobility simulation engine dtalite-s: A discretized space–time network-based modeling framework for bridging multi-agent simulation and optimization. Urban Rail Transit 5, 1–16 (2019) (29) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: An open urban driving simulator. In: Conference on Robot Learning, pp. 1–16 (2017). PMLR (30) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Mullakkal-Babu, F.A., Wang, M., van Arem, B., Shyrokau, B., Happee, R.: A hybrid submicroscopic-microscopic traffic flow simulation framework. IEEE Transactions on Intelligent Transportation Systems 22(6), 3430–3443 (2020) (31) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- de Souza, F., Verbas, O., Auld, J.: Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science 151, 858–863 (2019) (32) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Oppe, S.: Macroscopic models for traffic and traffic safety. Accident Analysis & Prevention 21(3), 225–232 (1989) (33) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer networks 182, 107484 (2020) (34) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Masek, P., Masek, J., Frantik, P., Fujdiak, R., Ometov, A., Hosek, J., Andreev, S., Mlynek, P., Misurec, J.: A harmonized perspective on transportation management in smart cities: The novel iot-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016) (35) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Maroto, J., Delso, E., Felez, J., Cabanellas, J.M.: Real-time traffic simulation with a microscopic model. IEEE Transactions on Intelligent Transportation Systems 7(4), 513–527 (2006) (36) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- NVIDIA: Simulation for self-driving vehicles (2023) (37) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al.: Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research. arXiv preprint arXiv:2310.08710 (2023) (38) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo–simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation (2011). ThinkMind (39) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Qadri, S.S.S.M., Gökçe, M.A., Öner, E.: State-of-art review of traffic signal control methods: challenges and opportunities. European transport research review 12, 1–23 (2020) (40) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Wei, H., Zheng, G., Yao, H., Li, Z.: Intellilight: A reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2496–2505 (2018) (41) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Willumsen, L.G.: Estimation of an od matrix from traffic counts–a review (1978) (42) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Abrahamsson, T.: Estimation of origin-destination matrices using traffic counts-a literature survey (1998) (43) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic matrix estimation: Existing techniques and new directions. ACM SIGCOMM Computer Communication Review 32(4), 161–174 (2002) (44) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Mahmassani, H.S.: Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics 1, 267–292 (2001) (45) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Mahmassani, H.S., Zhou, X.: In: Abed, E.H. (ed.) Transportation System Intelligence: Performance Measurement and Real-Time Traffic Estimation and Prediction in a Day-to-Day Learning Framework, pp. 305–328. Birkhäuser Boston, Boston, MA (2005) (46) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Zhou, X., Qin, X., Mahmassani, H.S.: Dynamic origin-destination demand estimation with multiday link traffic counts for planning applications. Transportation Research Record 1831(1), 30–38 (2003) (47) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Zhou, X., Erdoğan, S., Mahmassani, H.S.: Dynamic origin-destination trip demand estimation for subarea analysis. Transportation Research Record 1964(1), 176–184 (2006) (48) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Zhou, X., List, G.F.: An information-theoretic sensor location model for traffic origin-destination demand estimation applications. Transportation Science 44(2), 254–273 (2010) (49) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Zhou, X., Lu, C., Zhang, K.: Dynamic origin-destination demand flow estimation utilizing heterogeneous data sources under congested traffic conditions (2013) (50) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Krishnakumari, P., Van Lint, H., Djukic, T., Cats, O.: A data driven method for od matrix estimation. Transportation Research Part C: Emerging Technologies 113, 38–56 (2020) (51) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., Minbaleev, A.: Traffic flow estimation with data from a video surveillance camera. Journal of Big Data 6, 1–15 (2019) (52) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Pamuła, T., Żochowska, R.: Estimation and prediction of the od matrix in uncongested urban road network based on traffic flows using deep learning. Engineering Applications of Artificial Intelligence 117, 105550 (2023) (53) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Fu, H., Lam, W.H., Shao, H., Kattan, L., Salari, M.: Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects. Transportation Research Part E: Logistics and Transportation Review 157, 102555 (2022) (54) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Kumarage, S., Yildirimoglu, M., Zheng, Z.: A hybrid modelling framework for the estimation of dynamic origin–destination flows. Transportation Research Part B: Methodological 176, 102804 (2023) (55) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Mei, H., Lei, X., Da, L., Shi, B., Wei, H.: Libsignal: an open library for traffic signal control. Machine Learning, 1–37 (2023) (56) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Cools, S.-B., Gershenson, C., D’Hooghe, B.: Self-organizing traffic lights: A realistic simulation. Advances in applied self-organizing systems, 45–55 (2013) (57) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021) Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Wei, H., Zheng, G., Gayah, V., Li, Z.: Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation. ACM SIGKDD Explorations Newsletter 22(2), 12–18 (2021)
- Longchao Da (20 papers)
- Kuanru Liou (1 paper)
- Tiejin Chen (15 papers)
- Xuesong Zhou (6 papers)
- Xiangyong Luo (1 paper)
- Yezhou Yang (119 papers)
- Hua Wei (71 papers)