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An equilibrium-seeking search algorithm for integrating large-scale activity-based and dynamic traffic assignment models (2404.07789v1)
Published 11 Apr 2024 in cs.CY and physics.soc-ph
Abstract: This paper proposes an iterative methodology to integrate large-scale behavioral activity-based models with dynamic traffic assignment models. The main novelty of the proposed approach is the decoupling of the two parts, allowing the ex-post integration of any existing model as long as certain assumptions are satisfied. A measure of error is defined to characterize a search space easily explorable within its boundaries. Within it, a joint distribution of the number of trips and travel times is identified as the equilibrium distribution, i.e., the distribution for which trip numbers and travel times are bound in the neighborhood of the equilibrium between supply and demand. The approach is tested on a medium-sized city of 400,000 inhabitants and the results suggest that the proposed iterative approach does perform well, reaching equilibrium between demand and supply in a limited number of iterations thanks to its perturbation techniques. Overall, 15 iterations are needed to reach values of the measure of error lower than 10%. The equilibrium identified this way is then validated against baseline distributions to demonstrate the goodness of the results.
- Casas, J., Ferrer, J.L., Garcia, D., Perarnau, J., Torday, A.: Traffic simulation with Aimsun. In: Fundamentals of Traffic Simulation. Springer, New York, NY (2010) Fellendorf and Vortisch [2010] Fellendorf, M., Vortisch, P.: Microscopic traffic flow simulator VISSIM. In: Fundamentals of Traffic Simulation. Springer, New York, NY (2010) Krajzewicz [2010] Krajzewicz, D.: Traffic simulation with SUMO – Simulation of urban mobility. In: Fundamentals of Traffic Simulation. Springer, New York, NY (2010) Agriesti et al. [2022a] Agriesti, S., Roncoli, C., Nahmias-Biran, B.-h.: Assignment of a synthetic population for activity-based modeling employing publicly available data. International Journal of Geo-Information 11(2) (2022) Agriesti et al. [2022b] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: A Bayesian Optimization approach for calibrating large-scale activity-based transport models. https://arxiv.org/abs/2302.03480 (2022) Lin et al. [2008] Lin, D.-y., Eluru, N., Waller, T., Bhat, C.: Integration of activity-based modeling and dynamic traffic assignment. Transportation Research Record 2076 (2008) Bastarianto et al. [2023] Bastarianto, F.F., Hancock, T.O., Choudhury, C.F., E., M.: Agent-based models in urban transportation: review, challenges, and opportunities. European Transport Research Review 15(19) (2023) Xiong et al. [2018] Xiong, C., Zhou, X., Zhang, L.: AgBM-DTALite: An integrated modeling system of agent-based travel behaviour and transportation network dynamics. Travel Behaviour and Society 12, 141–150 (2018) Xiong et al. [2021] Xiong, C., Yange, X.T., Zhang, L., Lee, M., Zhou, W., Raqib, M.: An integrated modeling framework for active traffic management and its applications in the Washington, DC area. Journal of Intelligent Transportation Systems 25(6), 609–625 (2021) Zhang et al. [2018] Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Fellendorf, M., Vortisch, P.: Microscopic traffic flow simulator VISSIM. In: Fundamentals of Traffic Simulation. Springer, New York, NY (2010) Krajzewicz [2010] Krajzewicz, D.: Traffic simulation with SUMO – Simulation of urban mobility. In: Fundamentals of Traffic Simulation. Springer, New York, NY (2010) Agriesti et al. [2022a] Agriesti, S., Roncoli, C., Nahmias-Biran, B.-h.: Assignment of a synthetic population for activity-based modeling employing publicly available data. International Journal of Geo-Information 11(2) (2022) Agriesti et al. [2022b] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: A Bayesian Optimization approach for calibrating large-scale activity-based transport models. https://arxiv.org/abs/2302.03480 (2022) Lin et al. [2008] Lin, D.-y., Eluru, N., Waller, T., Bhat, C.: Integration of activity-based modeling and dynamic traffic assignment. Transportation Research Record 2076 (2008) Bastarianto et al. [2023] Bastarianto, F.F., Hancock, T.O., Choudhury, C.F., E., M.: Agent-based models in urban transportation: review, challenges, and opportunities. European Transport Research Review 15(19) (2023) Xiong et al. [2018] Xiong, C., Zhou, X., Zhang, L.: AgBM-DTALite: An integrated modeling system of agent-based travel behaviour and transportation network dynamics. Travel Behaviour and Society 12, 141–150 (2018) Xiong et al. [2021] Xiong, C., Yange, X.T., Zhang, L., Lee, M., Zhou, W., Raqib, M.: An integrated modeling framework for active traffic management and its applications in the Washington, DC area. Journal of Intelligent Transportation Systems 25(6), 609–625 (2021) Zhang et al. [2018] Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Krajzewicz, D.: Traffic simulation with SUMO – Simulation of urban mobility. In: Fundamentals of Traffic Simulation. Springer, New York, NY (2010) Agriesti et al. [2022a] Agriesti, S., Roncoli, C., Nahmias-Biran, B.-h.: Assignment of a synthetic population for activity-based modeling employing publicly available data. International Journal of Geo-Information 11(2) (2022) Agriesti et al. [2022b] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: A Bayesian Optimization approach for calibrating large-scale activity-based transport models. https://arxiv.org/abs/2302.03480 (2022) Lin et al. [2008] Lin, D.-y., Eluru, N., Waller, T., Bhat, C.: Integration of activity-based modeling and dynamic traffic assignment. Transportation Research Record 2076 (2008) Bastarianto et al. [2023] Bastarianto, F.F., Hancock, T.O., Choudhury, C.F., E., M.: Agent-based models in urban transportation: review, challenges, and opportunities. European Transport Research Review 15(19) (2023) Xiong et al. [2018] Xiong, C., Zhou, X., Zhang, L.: AgBM-DTALite: An integrated modeling system of agent-based travel behaviour and transportation network dynamics. Travel Behaviour and Society 12, 141–150 (2018) Xiong et al. [2021] Xiong, C., Yange, X.T., Zhang, L., Lee, M., Zhou, W., Raqib, M.: An integrated modeling framework for active traffic management and its applications in the Washington, DC area. Journal of Intelligent Transportation Systems 25(6), 609–625 (2021) Zhang et al. [2018] Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Agriesti, S., Roncoli, C., Nahmias-Biran, B.-h.: Assignment of a synthetic population for activity-based modeling employing publicly available data. International Journal of Geo-Information 11(2) (2022) Agriesti et al. [2022b] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: A Bayesian Optimization approach for calibrating large-scale activity-based transport models. https://arxiv.org/abs/2302.03480 (2022) Lin et al. [2008] Lin, D.-y., Eluru, N., Waller, T., Bhat, C.: Integration of activity-based modeling and dynamic traffic assignment. Transportation Research Record 2076 (2008) Bastarianto et al. [2023] Bastarianto, F.F., Hancock, T.O., Choudhury, C.F., E., M.: Agent-based models in urban transportation: review, challenges, and opportunities. European Transport Research Review 15(19) (2023) Xiong et al. [2018] Xiong, C., Zhou, X., Zhang, L.: AgBM-DTALite: An integrated modeling system of agent-based travel behaviour and transportation network dynamics. Travel Behaviour and Society 12, 141–150 (2018) Xiong et al. [2021] Xiong, C., Yange, X.T., Zhang, L., Lee, M., Zhou, W., Raqib, M.: An integrated modeling framework for active traffic management and its applications in the Washington, DC area. Journal of Intelligent Transportation Systems 25(6), 609–625 (2021) Zhang et al. [2018] Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: A Bayesian Optimization approach for calibrating large-scale activity-based transport models. https://arxiv.org/abs/2302.03480 (2022) Lin et al. [2008] Lin, D.-y., Eluru, N., Waller, T., Bhat, C.: Integration of activity-based modeling and dynamic traffic assignment. Transportation Research Record 2076 (2008) Bastarianto et al. [2023] Bastarianto, F.F., Hancock, T.O., Choudhury, C.F., E., M.: Agent-based models in urban transportation: review, challenges, and opportunities. European Transport Research Review 15(19) (2023) Xiong et al. [2018] Xiong, C., Zhou, X., Zhang, L.: AgBM-DTALite: An integrated modeling system of agent-based travel behaviour and transportation network dynamics. Travel Behaviour and Society 12, 141–150 (2018) Xiong et al. [2021] Xiong, C., Yange, X.T., Zhang, L., Lee, M., Zhou, W., Raqib, M.: An integrated modeling framework for active traffic management and its applications in the Washington, DC area. Journal of Intelligent Transportation Systems 25(6), 609–625 (2021) Zhang et al. [2018] Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Lin, D.-y., Eluru, N., Waller, T., Bhat, C.: Integration of activity-based modeling and dynamic traffic assignment. Transportation Research Record 2076 (2008) Bastarianto et al. [2023] Bastarianto, F.F., Hancock, T.O., Choudhury, C.F., E., M.: Agent-based models in urban transportation: review, challenges, and opportunities. European Transport Research Review 15(19) (2023) Xiong et al. [2018] Xiong, C., Zhou, X., Zhang, L.: AgBM-DTALite: An integrated modeling system of agent-based travel behaviour and transportation network dynamics. Travel Behaviour and Society 12, 141–150 (2018) Xiong et al. [2021] Xiong, C., Yange, X.T., Zhang, L., Lee, M., Zhou, W., Raqib, M.: An integrated modeling framework for active traffic management and its applications in the Washington, DC area. Journal of Intelligent Transportation Systems 25(6), 609–625 (2021) Zhang et al. [2018] Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Bastarianto, F.F., Hancock, T.O., Choudhury, C.F., E., M.: Agent-based models in urban transportation: review, challenges, and opportunities. European Transport Research Review 15(19) (2023) Xiong et al. [2018] Xiong, C., Zhou, X., Zhang, L.: AgBM-DTALite: An integrated modeling system of agent-based travel behaviour and transportation network dynamics. Travel Behaviour and Society 12, 141–150 (2018) Xiong et al. [2021] Xiong, C., Yange, X.T., Zhang, L., Lee, M., Zhou, W., Raqib, M.: An integrated modeling framework for active traffic management and its applications in the Washington, DC area. Journal of Intelligent Transportation Systems 25(6), 609–625 (2021) Zhang et al. [2018] Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Xiong, C., Zhou, X., Zhang, L.: AgBM-DTALite: An integrated modeling system of agent-based travel behaviour and transportation network dynamics. Travel Behaviour and Society 12, 141–150 (2018) Xiong et al. [2021] Xiong, C., Yange, X.T., Zhang, L., Lee, M., Zhou, W., Raqib, M.: An integrated modeling framework for active traffic management and its applications in the Washington, DC area. Journal of Intelligent Transportation Systems 25(6), 609–625 (2021) Zhang et al. [2018] Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Xiong, C., Yange, X.T., Zhang, L., Lee, M., Zhou, W., Raqib, M.: An integrated modeling framework for active traffic management and its applications in the Washington, DC area. Journal of Intelligent Transportation Systems 25(6), 609–625 (2021) Zhang et al. [2018] Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. 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Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. 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[2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). 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[2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. 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[2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Krajzewicz, D.: Traffic simulation with SUMO – Simulation of urban mobility. In: Fundamentals of Traffic Simulation. Springer, New York, NY (2010) Agriesti et al. [2022a] Agriesti, S., Roncoli, C., Nahmias-Biran, B.-h.: Assignment of a synthetic population for activity-based modeling employing publicly available data. International Journal of Geo-Information 11(2) (2022) Agriesti et al. [2022b] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: A Bayesian Optimization approach for calibrating large-scale activity-based transport models. https://arxiv.org/abs/2302.03480 (2022) Lin et al. [2008] Lin, D.-y., Eluru, N., Waller, T., Bhat, C.: Integration of activity-based modeling and dynamic traffic assignment. Transportation Research Record 2076 (2008) Bastarianto et al. [2023] Bastarianto, F.F., Hancock, T.O., Choudhury, C.F., E., M.: Agent-based models in urban transportation: review, challenges, and opportunities. European Transport Research Review 15(19) (2023) Xiong et al. [2018] Xiong, C., Zhou, X., Zhang, L.: AgBM-DTALite: An integrated modeling system of agent-based travel behaviour and transportation network dynamics. Travel Behaviour and Society 12, 141–150 (2018) Xiong et al. [2021] Xiong, C., Yange, X.T., Zhang, L., Lee, M., Zhou, W., Raqib, M.: An integrated modeling framework for active traffic management and its applications in the Washington, DC area. Journal of Intelligent Transportation Systems 25(6), 609–625 (2021) Zhang et al. [2018] Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Agriesti, S., Roncoli, C., Nahmias-Biran, B.-h.: Assignment of a synthetic population for activity-based modeling employing publicly available data. International Journal of Geo-Information 11(2) (2022) Agriesti et al. [2022b] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: A Bayesian Optimization approach for calibrating large-scale activity-based transport models. https://arxiv.org/abs/2302.03480 (2022) Lin et al. [2008] Lin, D.-y., Eluru, N., Waller, T., Bhat, C.: Integration of activity-based modeling and dynamic traffic assignment. Transportation Research Record 2076 (2008) Bastarianto et al. [2023] Bastarianto, F.F., Hancock, T.O., Choudhury, C.F., E., M.: Agent-based models in urban transportation: review, challenges, and opportunities. European Transport Research Review 15(19) (2023) Xiong et al. [2018] Xiong, C., Zhou, X., Zhang, L.: AgBM-DTALite: An integrated modeling system of agent-based travel behaviour and transportation network dynamics. Travel Behaviour and Society 12, 141–150 (2018) Xiong et al. [2021] Xiong, C., Yange, X.T., Zhang, L., Lee, M., Zhou, W., Raqib, M.: An integrated modeling framework for active traffic management and its applications in the Washington, DC area. Journal of Intelligent Transportation Systems 25(6), 609–625 (2021) Zhang et al. [2018] Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: A Bayesian Optimization approach for calibrating large-scale activity-based transport models. https://arxiv.org/abs/2302.03480 (2022) Lin et al. [2008] Lin, D.-y., Eluru, N., Waller, T., Bhat, C.: Integration of activity-based modeling and dynamic traffic assignment. Transportation Research Record 2076 (2008) Bastarianto et al. [2023] Bastarianto, F.F., Hancock, T.O., Choudhury, C.F., E., M.: Agent-based models in urban transportation: review, challenges, and opportunities. European Transport Research Review 15(19) (2023) Xiong et al. [2018] Xiong, C., Zhou, X., Zhang, L.: AgBM-DTALite: An integrated modeling system of agent-based travel behaviour and transportation network dynamics. Travel Behaviour and Society 12, 141–150 (2018) Xiong et al. [2021] Xiong, C., Yange, X.T., Zhang, L., Lee, M., Zhou, W., Raqib, M.: An integrated modeling framework for active traffic management and its applications in the Washington, DC area. Journal of Intelligent Transportation Systems 25(6), 609–625 (2021) Zhang et al. [2018] Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Lin, D.-y., Eluru, N., Waller, T., Bhat, C.: Integration of activity-based modeling and dynamic traffic assignment. Transportation Research Record 2076 (2008) Bastarianto et al. [2023] Bastarianto, F.F., Hancock, T.O., Choudhury, C.F., E., M.: Agent-based models in urban transportation: review, challenges, and opportunities. European Transport Research Review 15(19) (2023) Xiong et al. [2018] Xiong, C., Zhou, X., Zhang, L.: AgBM-DTALite: An integrated modeling system of agent-based travel behaviour and transportation network dynamics. Travel Behaviour and Society 12, 141–150 (2018) Xiong et al. [2021] Xiong, C., Yange, X.T., Zhang, L., Lee, M., Zhou, W., Raqib, M.: An integrated modeling framework for active traffic management and its applications in the Washington, DC area. Journal of Intelligent Transportation Systems 25(6), 609–625 (2021) Zhang et al. [2018] Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Bastarianto, F.F., Hancock, T.O., Choudhury, C.F., E., M.: Agent-based models in urban transportation: review, challenges, and opportunities. European Transport Research Review 15(19) (2023) Xiong et al. [2018] Xiong, C., Zhou, X., Zhang, L.: AgBM-DTALite: An integrated modeling system of agent-based travel behaviour and transportation network dynamics. Travel Behaviour and Society 12, 141–150 (2018) Xiong et al. [2021] Xiong, C., Yange, X.T., Zhang, L., Lee, M., Zhou, W., Raqib, M.: An integrated modeling framework for active traffic management and its applications in the Washington, DC area. Journal of Intelligent Transportation Systems 25(6), 609–625 (2021) Zhang et al. [2018] Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Xiong, C., Zhou, X., Zhang, L.: AgBM-DTALite: An integrated modeling system of agent-based travel behaviour and transportation network dynamics. Travel Behaviour and Society 12, 141–150 (2018) Xiong et al. [2021] Xiong, C., Yange, X.T., Zhang, L., Lee, M., Zhou, W., Raqib, M.: An integrated modeling framework for active traffic management and its applications in the Washington, DC area. Journal of Intelligent Transportation Systems 25(6), 609–625 (2021) Zhang et al. [2018] Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Xiong, C., Yange, X.T., Zhang, L., Lee, M., Zhou, W., Raqib, M.: An integrated modeling framework for active traffic management and its applications in the Washington, DC area. Journal of Intelligent Transportation Systems 25(6), 609–625 (2021) Zhang et al. [2018] Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). 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[2021] Xiong, C., Yange, X.T., Zhang, L., Lee, M., Zhou, W., Raqib, M.: An integrated modeling framework for active traffic management and its applications in the Washington, DC area. Journal of Intelligent Transportation Systems 25(6), 609–625 (2021) Zhang et al. [2018] Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: A Bayesian Optimization approach for calibrating large-scale activity-based transport models. https://arxiv.org/abs/2302.03480 (2022) Lin et al. [2008] Lin, D.-y., Eluru, N., Waller, T., Bhat, C.: Integration of activity-based modeling and dynamic traffic assignment. Transportation Research Record 2076 (2008) Bastarianto et al. [2023] Bastarianto, F.F., Hancock, T.O., Choudhury, C.F., E., M.: Agent-based models in urban transportation: review, challenges, and opportunities. European Transport Research Review 15(19) (2023) Xiong et al. [2018] Xiong, C., Zhou, X., Zhang, L.: AgBM-DTALite: An integrated modeling system of agent-based travel behaviour and transportation network dynamics. Travel Behaviour and Society 12, 141–150 (2018) Xiong et al. [2021] Xiong, C., Yange, X.T., Zhang, L., Lee, M., Zhou, W., Raqib, M.: An integrated modeling framework for active traffic management and its applications in the Washington, DC area. Journal of Intelligent Transportation Systems 25(6), 609–625 (2021) Zhang et al. [2018] Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Lin, D.-y., Eluru, N., Waller, T., Bhat, C.: Integration of activity-based modeling and dynamic traffic assignment. Transportation Research Record 2076 (2008) Bastarianto et al. [2023] Bastarianto, F.F., Hancock, T.O., Choudhury, C.F., E., M.: Agent-based models in urban transportation: review, challenges, and opportunities. European Transport Research Review 15(19) (2023) Xiong et al. [2018] Xiong, C., Zhou, X., Zhang, L.: AgBM-DTALite: An integrated modeling system of agent-based travel behaviour and transportation network dynamics. Travel Behaviour and Society 12, 141–150 (2018) Xiong et al. [2021] Xiong, C., Yange, X.T., Zhang, L., Lee, M., Zhou, W., Raqib, M.: An integrated modeling framework for active traffic management and its applications in the Washington, DC area. Journal of Intelligent Transportation Systems 25(6), 609–625 (2021) Zhang et al. [2018] Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Bastarianto, F.F., Hancock, T.O., Choudhury, C.F., E., M.: Agent-based models in urban transportation: review, challenges, and opportunities. European Transport Research Review 15(19) (2023) Xiong et al. [2018] Xiong, C., Zhou, X., Zhang, L.: AgBM-DTALite: An integrated modeling system of agent-based travel behaviour and transportation network dynamics. Travel Behaviour and Society 12, 141–150 (2018) Xiong et al. [2021] Xiong, C., Yange, X.T., Zhang, L., Lee, M., Zhou, W., Raqib, M.: An integrated modeling framework for active traffic management and its applications in the Washington, DC area. Journal of Intelligent Transportation Systems 25(6), 609–625 (2021) Zhang et al. [2018] Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Xiong, C., Zhou, X., Zhang, L.: AgBM-DTALite: An integrated modeling system of agent-based travel behaviour and transportation network dynamics. Travel Behaviour and Society 12, 141–150 (2018) Xiong et al. [2021] Xiong, C., Yange, X.T., Zhang, L., Lee, M., Zhou, W., Raqib, M.: An integrated modeling framework for active traffic management and its applications in the Washington, DC area. Journal of Intelligent Transportation Systems 25(6), 609–625 (2021) Zhang et al. [2018] Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Xiong, C., Yange, X.T., Zhang, L., Lee, M., Zhou, W., Raqib, M.: An integrated modeling framework for active traffic management and its applications in the Washington, DC area. Journal of Intelligent Transportation Systems 25(6), 609–625 (2021) Zhang et al. [2018] Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. 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[2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017)
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[2018] Xiong, C., Zhou, X., Zhang, L.: AgBM-DTALite: An integrated modeling system of agent-based travel behaviour and transportation network dynamics. Travel Behaviour and Society 12, 141–150 (2018) Xiong et al. [2021] Xiong, C., Yange, X.T., Zhang, L., Lee, M., Zhou, W., Raqib, M.: An integrated modeling framework for active traffic management and its applications in the Washington, DC area. Journal of Intelligent Transportation Systems 25(6), 609–625 (2021) Zhang et al. [2018] Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. 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[2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: A Bayesian Optimization approach for calibrating large-scale activity-based transport models. https://arxiv.org/abs/2302.03480 (2022) Lin et al. [2008] Lin, D.-y., Eluru, N., Waller, T., Bhat, C.: Integration of activity-based modeling and dynamic traffic assignment. Transportation Research Record 2076 (2008) Bastarianto et al. [2023] Bastarianto, F.F., Hancock, T.O., Choudhury, C.F., E., M.: Agent-based models in urban transportation: review, challenges, and opportunities. European Transport Research Review 15(19) (2023) Xiong et al. [2018] Xiong, C., Zhou, X., Zhang, L.: AgBM-DTALite: An integrated modeling system of agent-based travel behaviour and transportation network dynamics. Travel Behaviour and Society 12, 141–150 (2018) Xiong et al. [2021] Xiong, C., Yange, X.T., Zhang, L., Lee, M., Zhou, W., Raqib, M.: An integrated modeling framework for active traffic management and its applications in the Washington, DC area. Journal of Intelligent Transportation Systems 25(6), 609–625 (2021) Zhang et al. [2018] Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Lin, D.-y., Eluru, N., Waller, T., Bhat, C.: Integration of activity-based modeling and dynamic traffic assignment. Transportation Research Record 2076 (2008) Bastarianto et al. [2023] Bastarianto, F.F., Hancock, T.O., Choudhury, C.F., E., M.: Agent-based models in urban transportation: review, challenges, and opportunities. European Transport Research Review 15(19) (2023) Xiong et al. [2018] Xiong, C., Zhou, X., Zhang, L.: AgBM-DTALite: An integrated modeling system of agent-based travel behaviour and transportation network dynamics. Travel Behaviour and Society 12, 141–150 (2018) Xiong et al. [2021] Xiong, C., Yange, X.T., Zhang, L., Lee, M., Zhou, W., Raqib, M.: An integrated modeling framework for active traffic management and its applications in the Washington, DC area. Journal of Intelligent Transportation Systems 25(6), 609–625 (2021) Zhang et al. [2018] Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Bastarianto, F.F., Hancock, T.O., Choudhury, C.F., E., M.: Agent-based models in urban transportation: review, challenges, and opportunities. European Transport Research Review 15(19) (2023) Xiong et al. [2018] Xiong, C., Zhou, X., Zhang, L.: AgBM-DTALite: An integrated modeling system of agent-based travel behaviour and transportation network dynamics. Travel Behaviour and Society 12, 141–150 (2018) Xiong et al. [2021] Xiong, C., Yange, X.T., Zhang, L., Lee, M., Zhou, W., Raqib, M.: An integrated modeling framework for active traffic management and its applications in the Washington, DC area. Journal of Intelligent Transportation Systems 25(6), 609–625 (2021) Zhang et al. [2018] Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Xiong, C., Zhou, X., Zhang, L.: AgBM-DTALite: An integrated modeling system of agent-based travel behaviour and transportation network dynamics. Travel Behaviour and Society 12, 141–150 (2018) Xiong et al. [2021] Xiong, C., Yange, X.T., Zhang, L., Lee, M., Zhou, W., Raqib, M.: An integrated modeling framework for active traffic management and its applications in the Washington, DC area. Journal of Intelligent Transportation Systems 25(6), 609–625 (2021) Zhang et al. [2018] Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Xiong, C., Yange, X.T., Zhang, L., Lee, M., Zhou, W., Raqib, M.: An integrated modeling framework for active traffic management and its applications in the Washington, DC area. Journal of Intelligent Transportation Systems 25(6), 609–625 (2021) Zhang et al. [2018] Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). 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In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Lin, D.-y., Eluru, N., Waller, T., Bhat, C.: Integration of activity-based modeling and dynamic traffic assignment. Transportation Research Record 2076 (2008) Bastarianto et al. [2023] Bastarianto, F.F., Hancock, T.O., Choudhury, C.F., E., M.: Agent-based models in urban transportation: review, challenges, and opportunities. European Transport Research Review 15(19) (2023) Xiong et al. [2018] Xiong, C., Zhou, X., Zhang, L.: AgBM-DTALite: An integrated modeling system of agent-based travel behaviour and transportation network dynamics. Travel Behaviour and Society 12, 141–150 (2018) Xiong et al. [2021] Xiong, C., Yange, X.T., Zhang, L., Lee, M., Zhou, W., Raqib, M.: An integrated modeling framework for active traffic management and its applications in the Washington, DC area. Journal of Intelligent Transportation Systems 25(6), 609–625 (2021) Zhang et al. [2018] Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Bastarianto, F.F., Hancock, T.O., Choudhury, C.F., E., M.: Agent-based models in urban transportation: review, challenges, and opportunities. European Transport Research Review 15(19) (2023) Xiong et al. [2018] Xiong, C., Zhou, X., Zhang, L.: AgBM-DTALite: An integrated modeling system of agent-based travel behaviour and transportation network dynamics. Travel Behaviour and Society 12, 141–150 (2018) Xiong et al. [2021] Xiong, C., Yange, X.T., Zhang, L., Lee, M., Zhou, W., Raqib, M.: An integrated modeling framework for active traffic management and its applications in the Washington, DC area. Journal of Intelligent Transportation Systems 25(6), 609–625 (2021) Zhang et al. [2018] Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Xiong, C., Zhou, X., Zhang, L.: AgBM-DTALite: An integrated modeling system of agent-based travel behaviour and transportation network dynamics. Travel Behaviour and Society 12, 141–150 (2018) Xiong et al. [2021] Xiong, C., Yange, X.T., Zhang, L., Lee, M., Zhou, W., Raqib, M.: An integrated modeling framework for active traffic management and its applications in the Washington, DC area. Journal of Intelligent Transportation Systems 25(6), 609–625 (2021) Zhang et al. [2018] Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Xiong, C., Yange, X.T., Zhang, L., Lee, M., Zhou, W., Raqib, M.: An integrated modeling framework for active traffic management and its applications in the Washington, DC area. Journal of Intelligent Transportation Systems 25(6), 609–625 (2021) Zhang et al. [2018] Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. 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Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Bastarianto, F.F., Hancock, T.O., Choudhury, C.F., E., M.: Agent-based models in urban transportation: review, challenges, and opportunities. European Transport Research Review 15(19) (2023) Xiong et al. [2018] Xiong, C., Zhou, X., Zhang, L.: AgBM-DTALite: An integrated modeling system of agent-based travel behaviour and transportation network dynamics. Travel Behaviour and Society 12, 141–150 (2018) Xiong et al. [2021] Xiong, C., Yange, X.T., Zhang, L., Lee, M., Zhou, W., Raqib, M.: An integrated modeling framework for active traffic management and its applications in the Washington, DC area. Journal of Intelligent Transportation Systems 25(6), 609–625 (2021) Zhang et al. [2018] Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Xiong, C., Zhou, X., Zhang, L.: AgBM-DTALite: An integrated modeling system of agent-based travel behaviour and transportation network dynamics. Travel Behaviour and Society 12, 141–150 (2018) Xiong et al. [2021] Xiong, C., Yange, X.T., Zhang, L., Lee, M., Zhou, W., Raqib, M.: An integrated modeling framework for active traffic management and its applications in the Washington, DC area. Journal of Intelligent Transportation Systems 25(6), 609–625 (2021) Zhang et al. [2018] Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Xiong, C., Yange, X.T., Zhang, L., Lee, M., Zhou, W., Raqib, M.: An integrated modeling framework for active traffic management and its applications in the Washington, DC area. Journal of Intelligent Transportation Systems 25(6), 609–625 (2021) Zhang et al. [2018] Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. 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[1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Xiong, C., Zhou, X., Zhang, L.: AgBM-DTALite: An integrated modeling system of agent-based travel behaviour and transportation network dynamics. Travel Behaviour and Society 12, 141–150 (2018) Xiong et al. [2021] Xiong, C., Yange, X.T., Zhang, L., Lee, M., Zhou, W., Raqib, M.: An integrated modeling framework for active traffic management and its applications in the Washington, DC area. Journal of Intelligent Transportation Systems 25(6), 609–625 (2021) Zhang et al. [2018] Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Xiong, C., Yange, X.T., Zhang, L., Lee, M., Zhou, W., Raqib, M.: An integrated modeling framework for active traffic management and its applications in the Washington, DC area. Journal of Intelligent Transportation Systems 25(6), 609–625 (2021) Zhang et al. [2018] Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017)
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[2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Xiong, C., Yange, X.T., Zhang, L., Lee, M., Zhou, W., Raqib, M.: An integrated modeling framework for active traffic management and its applications in the Washington, DC area. Journal of Intelligent Transportation Systems 25(6), 609–625 (2021) Zhang et al. [2018] Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. 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[2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. 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In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. 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[1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. 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Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Rodrigue, J.P.: Transportation, Economy and Society. 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Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. 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[2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017)
- Zhang, L., Yang, D., Ghader, S., Carrion, C., Xiong, C., Rossi, T.F., Milkovits, M., Mahapatra, S., Barber, C.: An integrated, validated and applied activity-based dynamic traffic assignment model for the Baltimore-Washington region. Transportation Research Record 2672(51), 45–55 (2018) Pendyala et al. [2012] Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M.: An integrated land use–transport model system with dynamic time-dependent activity-travel microsimulation. Transportation Research Record 2303(1), 19–27 (2012) Pendyala et al. [2017] Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. 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[2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. 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Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Pendyala, R.M., You, D., Garikapati, V.M., Konduri, K.C., Zhou, X.: Paradigms for integrated modeling of activity-travel demand and network dynamics in an era of dynamic mobility management. In: Transportation Research Board 96th Annual Meeting (2017) Heinrichs et al. [2018] Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). 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Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017)
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In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Heinrichs, M., Behrisch, M., Erdmann, J.: Just do it! combining agent-based travel demand models with queue based-traffic flow models. Procedia Computer Science 130, 858–864 (2018) Goulias et al. [2011] Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. 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[2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. 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[2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. 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Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017)
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[2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Goulias, K.G., Bhat, C.R., Pendyala, R.M., Chen, Y., Paleti, R., Konduri, K.C., Huang, G., Hu, H.-h.: Simulator of activities, greenhouse emissions, networks and travel (SimAGENT) in Southern California: Design, implementation, preliminary findings and integration plans. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 164–169 (2011) Flötteröd et al. [2012] Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Flötteröd, G., Chen, Y., Nagel, K.: Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481–502 (2012) Horni et al. [2016] Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. 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[2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. 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Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. 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[2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). 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[2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017)
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In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim. Ubiquity Press, London, UK (2016) Müller et al. [2022] Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Müller, J., Straub, M., Richter, G., Rudloff, C.: Integration of different mobility behaviors and intermodal trips in matsim. Sustainability 14(1) (2022) Novosel et al. [2015] Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. 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[2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. 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Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. 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[2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. 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[2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017)
- Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning – utilization of matsim for transport energy demand modelling. Energy 92, 466–475 (2015) Zieke et al. [2019] Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. 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In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zieke, D., Kaddoura, I., Nagel, K.: The MATSim open Berlin scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science 151, 870–877 (2019) Zieke et al. [2015] Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. 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The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017)
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[2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Zieke, D., Nagel, K., Bhat, C.: Integrating CEMDAP and MATSim to increase the transferability of transport demand models. Transportation Research Record 2493(1), 117–125 (2015) Lu et al. [2015] Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. 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[2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. 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Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. 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[2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017)
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In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017)
- Lu, Y., Basak, K., Carrion, C., Loganathan, H., Adnan, M., Pereira, F.C., Saber, V.H., Ben-Akiva, M.: Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. In: Transportation Research Board 94th Annual Meeting (2015) Basu et al. [2018] Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. 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[2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017)
- Basu, R., Araldo, A., Akkinepally, A., Basak, K., Seshadri, R., Nahmias-Biran, B., Deshmukh, N., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Implementation & policy applications of AMOD in multimodal activity-driven agent-based urban simulator SimMobility. Transportation Research Record (2018) Marczuk et al. [2015] Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017)
- Marczuk, K.A., Hong, H.S.S., Azevedo, C.M.L., Adnan, M., Pendleton, S.D., Frazzoli, E., D.H., L.: Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. In: 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). (2015) Agriesti et al. [2023] Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Agriesti, S., Anashin, P., Roncoli, C., Nahmias-Biran, B.-h.: Integrating activity-based and traffic assignment models: Methodology and case study application. In: 2023 8th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2023) Rodrigue [2020] Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Rodrigue, J.P.: Transportation, Economy and Society. Taylor & Francis, London, UK (2020) Lourenço et al. [2003] Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. 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[2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. 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Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017)
- Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated Local Search vol. 57. Springer, Boston, MA (2003) Johnson et al. [1988] Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017)
- Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Sciences 37, 79–100 (1988) Nahmias-Biran et al. [2021] Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Nahmias-Biran, B., Oke, J.B., Kumar, N., Azevedo, C.L., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021) Aimsun [2022] Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. [2020] Hadachi, A., Pourmoradnasseri, M., Khoshkhah, K.: Unveiling large-scale commuting patterns based on mobile phone cellular network data. Journal of Transport Geography 89 (2020) Cavoli, C. [2017] Cavoli, C.: CREATE — City report Tallinn, Estonia. http://www.create-mobility.eu/create/resources/general/download/CITY-REPORT-Tallinn-WSWE-AV3MMA (2017) Aimsun: Aimsun Next User Manual. Aimsun SLU, Barcelona, Spain (2022). Aimsun SLU Beeston et al. [2021] Beeston, L., Blewitt, R., Bulmer, S., J., W.: Traffic Modeling Guidelines 4.0. Transport for London, London, UK (2021). Transport for London NumPy-Developers [2022] NumPy-Developers: NumPy.quantile. https://numpy.org/doc/stable/reference/generated/numpy.quantile.html (2022) Hyndman and Fan [1996] Hyndman, R.J., Fan, Y.: Sample quantiles in statistical packages. The American Statistician 50(4) (1996) Agriesti et al. [2023] Agriesti, S., Kuzmanovski, V., Hollmén, J., Roncoli, C., Nahmias-Biran, B.-h.: Calibration of large-scale behavioral transport models via Bayesian Optimization. In: 2023 Transportation Research Board 102nd Annual Meeting (2023) Hadachi et al. 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