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Incorporating Ridesharing in the Static Traffic Assignment Model

Author

Listed:
  • Oren Bahat

    (Technion – Israel Institute of Technology)

  • Shlomo Bekhor

    (Technion – Israel Institute of Technology)

Abstract

This paper develops a combined mode choice and traffic assignment model that incorporates ridesharing as an option in a mode choice model, attempting to quantify the ridesharing market share in an equilibrium context. The mode choice model takes into account that the waiting time for a ride is dependent on the available drivers. The traffic assignment model is a static user equilibrium that interacts with the discrete choice model through level of service variables. An iterative algorithm was implemented and applied in a simple network and a more realistic network. The results indicate that the quantity of ride sharing drivers is a key parameter to the service success, and below a critical mass of drivers, it is unlikely that passengers will find the service valuable. It is also shown that ride sharing has the ability to reduce in-vehicle times for all the users, although passenger may suffer from longer door-to-door times, having to wait for their ride.

Suggested Citation

  • Oren Bahat & Shlomo Bekhor, 2016. "Incorporating Ridesharing in the Static Traffic Assignment Model," Networks and Spatial Economics, Springer, vol. 16(4), pages 1125-1149, December.
    • Handle: RePEc:kap:netspa:v:16:y:2016:i:4:d:10.1007_s11067-015-9313-7
    DOI: 10.1007/s11067-015-9313-7
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    References listed on IDEAS

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    Cited by:

    1. Meng Li & Guowei Hua & Haijun Huang, 2018. "A Multi-Modal Route Choice Model with Ridesharing and Public Transit," Sustainability, MDPI, vol. 10(11), pages 1-14, November.
    2. André Palma & Lucas Javaudin & Patrick Stokkink & Léandre Tarpin-Pitre, 2024. "Ride-sharing with inflexible drivers in the Paris metropolitan area," Transportation, Springer, vol. 51(3), pages 963-986, June.
    3. Rui Yao & Shlomo Bekhor, 2023. "A general equilibrium model for multi-passenger ridesharing systems with stable matching," Papers 2303.16595, arXiv.org, revised Dec 2023.
    4. André de Palma & Patrick Stokkink & Nikolas Geroliminis, 2020. "Influence of Dynamic Congestion on Carpooling Matching," THEMA Working Papers 2020-12, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    5. Kitthamkesorn, Songyot & Chen, Anthony, 2017. "Alternate weibit-based model for assessing green transport systems with combined mode and route travel choices," Transportation Research Part B: Methodological, Elsevier, vol. 103(C), pages 291-310.
    6. Sun, S. & Szeto, W.Y., 2021. "Multi-class stochastic user equilibrium assignment model with ridesharing: Formulation and policy implications," Transportation Research Part A: Policy and Practice, Elsevier, vol. 145(C), pages 203-227.
    7. Xingyuan Li & Jing Bai, 2021. "A Ridesharing Choice Behavioral Equilibrium Model with Users of Heterogeneous Values of Time," IJERPH, MDPI, vol. 18(3), pages 1-22, January.
    8. Biao Yin & Liu Liu & Nicolas Coulombel & Vincent Viguie, 2018. "Appraising the environmental benefits of ride-sharing: The Paris region case study," Post-Print hal-01695082, HAL.
    9. de Palma, André & Stokkink, Patrick & Geroliminis, Nikolas, 2022. "Influence of dynamic congestion with scheduling preferences on carpooling matching with heterogeneous users," Transportation Research Part B: Methodological, Elsevier, vol. 155(C), pages 479-498.
    10. Rui Yao & Shlomo Bekhor, 2021. "A Dynamic Tree Algorithm for Peer-to-Peer Ridesharing Matching," Networks and Spatial Economics, Springer, vol. 21(4), pages 801-837, December.
    11. Guo, Yuhan & Zhang, Yu & Boulaksil, Youssef & Qian, Yaguan & Allaoui, Hamid, 2023. "Modelling and analysis of online ride-sharing platforms – A sustainability perspective," European Journal of Operational Research, Elsevier, vol. 304(2), pages 577-595.
    12. André de Palma & Lucas Javaudin & Patrick Stokkink & Léandre Tarpin-Pitre, 2021. "Modelling Ridesharing in a Large Network with Dynamic Congestion," THEMA Working Papers 2021-16, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    13. Chen-Yang Yan & Mao-Bin Hu & Rui Jiang & Jiancheng Long & Jin-Yong Chen & Hao-Xiang Liu, 2019. "Stochastic Ridesharing User Equilibrium in Transport Networks," Networks and Spatial Economics, Springer, vol. 19(4), pages 1007-1030, December.
    14. Yao, Rui & Bekhor, Shlomo, 2023. "A general equilibrium model for multi-passenger ridesharing systems with stable matching," Transportation Research Part B: Methodological, Elsevier, vol. 175(C).
    15. Eva Malichová & Ghadir Pourhashem & Tatiana Kováčiková & Martin Hudák, 2020. "Users’ Perception of Value of Travel Time and Value of Ridesharing Impacts on Europeans’ Ridesharing Participation Intention: A Case Study Based on MoTiV European-Wide Mobility and Behavioral Pattern ," Sustainability, MDPI, vol. 12(10), pages 1-19, May.
    16. Bilong Shen & Weimin Zheng & Kathleen M. Carley, 2018. "Urban Activity Mining Framework for Ride Sharing Systems Based on Vehicular Social Networks," Networks and Spatial Economics, Springer, vol. 18(3), pages 705-734, September.
    17. Ruijie Li & Yu (Marco) Nie & Xiaobo Liu, 2020. "Pricing Carpool Rides Based on Schedule Displacement," Transportation Science, INFORMS, vol. 54(4), pages 1134-1152, July.

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