Design of Transit-Centric Multimodal Urban Mobility System with Autonomous Mobility-on-Demand (2404.05885v1)
Abstract: This paper addresses the pressing challenge of urban mobility in the context of growing urban populations, changing demand patterns for urban mobility, and emerging technologies like Mobility-on-Demand (MoD) platforms and Autonomous Vehicle (AV). As urban areas swell and demand pattern changes, the integration of Autonomous Mobility-on-Demand (AMoD) systems with existing public transit (PT) networks presents great opportunities to enhancing urban mobility. We propose a novel optimization framework for solving the Transit-Centric Multimodal Urban Mobility with Autonomous Mobility-on-Demand (TCMUM-AMoD) at scale. The system operator (public transit agency) determines the network design and frequency settings of the PT network, fleet sizing and allocations of AMoD system, and the pricing for using the multimodal system with the goal of minimizing passenger disutility. Passengers' mode and route choice behaviors are modeled explicitly using discrete choice models. A first-order approximation algorithm is introduced to solve the problem at scale. Using a case study in Chicago, we showcase the potential to optimize urban mobility across different demand scenarios. To our knowledge, ours is the first paper to jointly optimize transit network design, fleet sizing, and pricing for the multimodal mobility system while considering passengers' mode and route choices.
- W. Lerner, “The future of urban mobility: towards networked, multimodal cities of 2050,” 2018. [Online]. Available: https://www.adlittle.com/sites/default/files/viewpoints/adl_the_future_of_urban_mobility_report.pdf
- U.S. Department of Transportation, “Public transportation’s role in responding to climate change,” 2010. [Online]. Available: https://www.transit.dot.gov/sites/fta.dot.gov/files/docs/PublicTransportationsRoleInRespondingToClimateChange2010.pdf
- B. Mo, Z. Cao, H. Zhang, Y. Shen, and J. Zhao, “Competition between shared autonomous vehicles and public transit: A case study in Singapore,” Transportation Research Part C: Emerging Technologies, vol. 127, no. April, p. 103058, 2021. [Online]. Available: https://doi.org/10.1016/j.trc.2021.103058
- A. Ceder and N. H. Wilson, “Bus network design,” Transportation Research Part B: Methodological, vol. 20, no. 4, pp. 331–344, 1986. [Online]. Available: https://www.sciencedirect.com/science/article/pii/0191261586900470
- O. Ibarra-Rojas, F. Delgado, R. Giesen, and J. Muñoz, “Planning, operation, and control of bus transport systems: A literature review,” Transportation Research Part B: Methodological, vol. 77, p. 38–75, Jul 2015.
- G. F. Newell, “Dispatching Policies for a Transportation Route,” Transportation Science, vol. 5, no. 1, pp. 91–105, 1971.
- P. G. Furth and N. H. Wilson, “Setting Frequencies on Bus Routes: Theory and Practice.” Transportation Research Record, pp. 1–7, 1981.
- I. Verbas and H. Mahmassani, “Optimal allocation of service frequencies over transit network routes and time periods,” Transportation Research Record, no. 2334, pp. 50–59, 2013.
- J. M. Barrero, N. Bloom, and S. J. Davis, “Why working from home will stick,” National Bureau of Economic Research, Working Paper 28731, April 2021. [Online]. Available: http://www.nber.org/papers/w28731
- N. S. Caros, X. Guo, and J. Zhao, “The emerging spectrum of flexible work locations: implications for travel demand and carbon emissions,” 2023.
- N. S. Caros, X. Guo, Y. Zheng, and J. Zhao, “The impacts of remote work on travel: insights from nearly three years of monthly surveys,” 2023.
- K. Gkiotsalitis, M. Schmidt, and E. van der Hurk, “Subline frequency setting for autonomous minibusses under demand uncertainty,” Transportation Research Part C: Emerging Technologies, vol. 135, p. 103492, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0968090X21004782
- X. Guo, B. Mo, H. N. Koutsopoulos, S. Wang, and J. Zhao, “Transit frequency setting problem with demand uncertainty,” 2022.
- H. Wang and H. Yang, “Ridesourcing systems: A framework and review,” Transportation Research Part B: Methodological, vol. 129, p. 122–155, Nov 2019.
- M. Young and S. Farber, “The who, why, and when of Uber and other ride-hailing trips: An examination of a large sample household travel survey,” Transportation Research Part A: Policy and Practice, vol. 119, no. November 2018, pp. 383–392, 2019. [Online]. Available: https://doi.org/10.1016/j.tra.2018.11.018
- Q. Wang, S. Wang, D. Zhuang, H. Koutsopoulos, and J. Zhao, “Uncertainty quantification of spatiotemporal travel demand with probabilistic graph neural networks,” IEEE Transactions on Intelligent Transportation Systems, pp. 1–12, 2024.
- J. V. Hall and A. B. Krueger, “An Analysis of the Labor Market for Uber’s Driver-Partners in the United States,” ILR Review, vol. 71, no. 3, pp. 705–732, 2018.
- J. C. Castillo, D. T. Knoepfle, and E. G. Weyl, “Matching in Ride Hailing: Wild Goose Chases and How to Solve Them,” SSRN Electronic Journal, 2022. [Online]. Available: https://papers.ssrn.com/abstract=2890666
- X. Guo, A. Haupt, H. Wang, R. Qadri, and J. Zhao, “Understanding multi-homing and switching by platform drivers,” Transportation Research Part C: Emerging Technologies, vol. 154, p. 104233, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0968090X2300222X
- M. C. Cohen and R. Zhang, “Competition and coopetition for two-sided platforms,” SSRN Working Paper No. 3028138, p. 48, 2017.
- H. Zhang, X. Guo, and J. Zhao, “Economies and diseconomies of scale in segmented mobility sharing markets,” 2022.
- X. Wang, Z. Zhao, H. Zhang, X. Guo, and J. Zhao, “Quantifying the uneven efficiency benefits of ridesharing market integration,” 2023.
- X. Guo, A. Qu, H. Zhang, P. Noursalehi, and J. Zhao, “Dissolving the segmentation of a shared mobility market: A framework and four market structure designs,” Transportation Research Part C: Emerging Technologies, vol. 157, p. 104397, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0968090X2300387X
- S. Banerjee, D. Freund, and T. Lykouris, “Pricing and optimization in shared vehicle systems: An approximation framework,” Operations Research, vol. 70, no. 3, pp. 1783–1805, 2022. [Online]. Available: https://doi.org/10.1287/opre.2021.2165
- J. Liu, W. Ma, and S. Qian, “Optimal curbside pricing for managing ride-hailing pick-ups and drop-offs,” Transportation Research Part C: Emerging Technologies, vol. 146, p. 103960, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0968090X22003734
- J. Alonso-Mora, S. Samaranayake, A. Wallar, E. Frazzoli, and D. Rus, “On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment,” Proceedings of the National Academy of Sciences, vol. 114, no. 3, pp. 462–467, 2017.
- D. Bertsimas, P. Jaillet, and S. Martin, “Online vehicle routing: The edge of optimization in large-scale applications,” Operations Research, vol. 67, no. 1, pp. 143–162, 2019. [Online]. Available: https://doi.org/10.1287/opre.2018.1763
- A. Tafreshian, N. Masoud, and Y. Yin, “Frontiers in service science: Ride matching for peer-to-peer ride sharing: A review and future directions,” Service Science, vol. 12, no. 2-3, pp. 44–60, 2020. [Online]. Available: https://doi.org/10.1287/serv.2020.0258
- J. Wen, J. Zhao, and P. Jaillet, “Rebalancing shared mobility-on-demand systems: A reinforcement learning approach,” in 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 2017, pp. 220–225.
- X. Guo, N. S. Caros, and J. Zhao, “Robust matching-integrated vehicle rebalancing in ride-hailing system with uncertain demand,” Transportation Research Part B: Methodological, vol. 150, pp. 161–189, 2021. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0191261521001004
- X. Guo, Q. Wang, and J. Zhao, “Data-driven vehicle rebalancing with predictive prescriptions in the ride-hailing system,” IEEE Open Journal of Intelligent Transportation Systems, vol. 3, pp. 251–266, 2022.
- X. Guo, H. Xu, D. Zhuang, Y. Zheng, and J. Zhao, “Fairness-enhancing vehicle rebalancing in the ride-hailing system,” 2023.
- G. Zardini, N. Lanzetti, M. Pavone, and E. Frazzoli, “Analysis and control of autonomous mobility-on-demand systems: A review,” arXiv:2106.14827 [cs, eess], Jun 2021, arXiv: 2106.14827. [Online]. Available: http://arxiv.org/abs/2106.14827
- R. Iglesias, F. Rossi, K. Wang, D. Hallac, J. Leskovec, and M. Pavone, “Data-driven model predictive control of autonomous mobility-on-demand systems,” arXiv:1709.07032 [cs, stat], Sep 2017, arXiv: 1709.07032. [Online]. Available: http://arxiv.org/abs/1709.07032
- M. Tsao, D. Milojevic, C. Ruch, M. Salazar, E. Frazzoli, and M. Pavone, “Model predictive control of ride-sharing autonomous mobility-on-demand systems,” in 2019 International Conference on Robotics and Automation (ICRA), 2019, pp. 6665–6671.
- Y. Shen, H. Zhang, and J. Zhao, “Integrating shared autonomous vehicle in public transportation system: A supply-side simulation of the first-mile service in singapore,” Transportation Research Part A: Policy and Practice, vol. 113, p. 125–136, Jul 2018.
- J. Wen, Y. X. Chen, N. Nassir, and J. Zhao, “Transit-oriented autonomous vehicle operation with integrated demand-supply interaction,” Transportation Research Part C: Emerging Technologies, vol. 97, p. 216–234, Dec 2018.
- M. Salazar, N. Lanzetti, F. Rossi, M. Schiffer, and M. Pavone, “Intermodal autonomous mobility-on-demand,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 9, pp. 3946–3960, 2020.
- Q. Luo, S. Li, and R. C. Hampshire, “Optimal design of intermodal mobility networks under uncertainty: Connecting micromobility with mobility-on-demand transit,” EURO Journal on Transportation and Logistics, vol. 10, no. June 2020, p. 100045, 2021. [Online]. Available: https://doi.org/10.1016/j.ejtl.2021.100045
- K. Steiner and S. Irnich, “Strategic planning for integrated mobility-on-demand and urban public bus networks,” Transportation Science, vol. 54, no. 6, pp. 1616–1639, 2020.
- H. K. Pinto, M. F. Hyland, H. S. Mahmassani, and I. Ö. Verbas, “Joint design of multimodal transit networks and shared autonomous mobility fleets,” Transportation Research Part C: Emerging Technologies, vol. 113, pp. 2–20, 2020. [Online]. Available: https://doi.org/10.1016/j.trc.2019.06.010.
- S. Banerjee, C. Hssaine, N. Périvier, and S. Samaranayake, “Real-time approximate routing for smart transit systems,” 2021.
- Q. Luo, S. Samaranayake, and S. Banerjee, “Multimodal mobility systems: joint optimization of transit network design and pricing,” in Proceedings of the ACM/IEEE 12th International Conference on Cyber-Physical Systems, ser. ICCPS ’21. New York, NY, USA: Association for Computing Machinery, 2021, p. 121–131. [Online]. Available: https://doi.org/10.1145/3450267.3450540
- Y. Wang, X. Lin, F. He, and M. Li, “Designing transit-oriented multi-modal transportation systems considering travelers’ choices,” Transportation Research Part B: Methodological, vol. 162, no. October 2021, pp. 292–327, 2022. [Online]. Available: https://doi.org/10.1016/j.trb.2022.06.002
- P. Kumar and A. Khani, “Planning of integrated mobility-on-demand and urban transit networks,” Transportation Research Part A: Policy and Practice, vol. 166, no. March, pp. 499–521, 2022. [Online]. Available: https://doi.org/10.1016/j.tra.2022.11.001
- D. Bertsimas, Y. Sian Ng, and J. Yan, “Joint frequency-setting and pricing optimization on multimodal transit networks at scale,” Transportation Science, vol. 54, no. 3, p. 839–853, May 2020.
- Y. Shen, H. Zhang, and J. Zhao, “Integrating shared autonomous vehicle in public transportation system: A supply-side simulation of the first-mile service in singapore,” Transportation Research Part A: Policy and Practice, vol. 113, pp. 125–136, 2018. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S096585641730681X
- D. McFadden, “The measurement of urban travel demand,” Journal of Public Economics, vol. 3, no. 4, pp. 303–328, 1974. [Online]. Available: https://www.sciencedirect.com/science/article/pii/0047272774900036
- Gurobi Optimization, LLC, “Gurobi Optimizer Reference Manual,” 2023. [Online]. Available: https://www.gurobi.com
- G. E. Sánchez-Martínez, “Inference of public transportation trip destinations by using fare transaction and vehicle location data: Dynamic programming approach,” Transportation Research Record, vol. 2652, no. 1, pp. 1–7, 2017. [Online]. Available: https://doi.org/10.3141/2652-01
- J. Zhao, A. Rahbee, and N. H. M. Wilson, “Estimating a rail passenger trip origin-destination matrix using automatic data collection systems,” Computer-Aided Civil and Infrastructure Engineering, vol. 22, no. 5, pp. 376–387, 2007. [Online]. Available: https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-8667.2007.00494.x
- N. S. Caros, X. Guo, A. Stewart, J. Attanucci, N. Smith, D. Nioras, A. Gartsman, and A. Zimmer, “Ridership and operations visualization engine: An integrated transit performance and passenger journey visualization engine,” Transportation Research Record, 2022. [Online]. Available: https://doi.org/10.1177/03611981221103232
- Brett Helling, “How much does uber cost? fare pricing, rates, and cost estimates explained,” 2024. [Online]. Available: https://www.ridester.com/uber-rates-cost/
- M. Hyland, C. Frei, A. Frei, and H. S. Mahmassani, “Riders on the storm: Exploring weather and seasonality effects on commute mode choice in chicago,” Travel Behaviour and Society, vol. 13, pp. 44–60, 10 2018, vOT.
- Transport for London, “Traffic modelling guidelines (version 4.0),” 2021. [Online]. Available: https://content.tfl.gov.uk/traffic-modelling-guidelines.pdf
- C. MacKechnie, “How much does a bus cost to purchase and operate?” 2019. [Online]. Available: https://www.liveabout.com/bus-cost-to-purchase-and-operate-2798845
- T. Mickle, Y. Lu, and M. Isaac, “This experience may feel futuristic’: Three rides in waymo robot taxis,” 2023. [Online]. Available: https://www.nytimes.com/2023/08/21/technology/waymo-driverless-cars-san-francisco.html?searchResultPosition=1