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A path-based equilibrium model for ridesharing matching

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  • Li, Yuanyuan
  • Liu, Yang
  • Xie, Jun

Abstract

This paper examines how the ridesharing program will reshape the spatial distribution of traffic congestion. With ridesharing services, travelers make multi-dimensional decisions concerning mode choices, route choices, and matching decisions. We build a path-based equilibrium model to describe the decision-making of travelers in the presence of the ridesharing program and thereby to forecast the stable congestion distribution in the long run. Specifically, our model can explicitly track the path information of the matched drivers and riders by solving the proposed equilibrium model. The model is formulated as variational inequalities due to the non-separable cost functions and asymmetric link interactions. We first demonstrate the non-uniqueness of mode-specific link flows and derive the condition for the uniqueness of link flows. We then derive sufficient conditions for a matching failure. A matching failure occurs if the lowest total inconvenience cost of a pair of rider and driver exceeds a threshold. In this situation, matching between the ridesharing supply (drivers) and demand (riders) cannot be established by adjusting the compensation scheme. Moreover, we derive the sufficient and necessary conditions for the existence of equilibrium with a positive ridesharing ridership. These conditions help operators gain insights into how to appropriately design compensation schemes. We reformulate the variational inequalities as the equivalent mixed complementarity problem, and propose a heuristic routing algorithm to identify the feasible routing paths for shared rides that interest both ridesharing drivers and riders. Finally, Our numerical experiments show that ridesharing is effective in relieving the overall congestion but leads to high congestion on some links. In the scenarios where the demand into the central business district (CBD) is high, the commuters entering CBD are all better off with ridesharing service. In contrast, the commuters traveling to certain non-CBD areas are worse off. Our experiments also reveal various path choice behaviors of ridesharing participants. The solo drivers always use the paths with short travel times, while the ridesharing drivers tend to use paths with short travel distances. By considering a commission to the platform, we demonstrate the regime in which increasing the commission rate improves the platform’s revenue without harming the social welfare.

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  • Li, Yuanyuan & Liu, Yang & Xie, Jun, 2020. "A path-based equilibrium model for ridesharing matching," Transportation Research Part B: Methodological, Elsevier, vol. 138(C), pages 373-405.
    • Handle: RePEc:eee:transb:v:138:y:2020:i:c:p:373-405
    DOI: 10.1016/j.trb.2020.05.007
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    as
    1. Qian, Xinwu & Ukkusuri, Satish V., 2017. "Taxi market equilibrium with third-party hailing service," Transportation Research Part B: Methodological, Elsevier, vol. 100(C), pages 43-63.
    2. Xing Wang & Niels Agatz & Alan Erera, 2018. "Stable Matching for Dynamic Ride-Sharing Systems," Transportation Science, INFORMS, vol. 52(4), pages 850-867, August.
    3. Xiaolei Wang & Hai Yang & Daoli Zhu, 2018. "Driver-Rider Cost-Sharing Strategies and Equilibria in a Ridesharing Program," Transportation Science, INFORMS, vol. 52(4), pages 868-881, August.
    4. Jin Y. Yen, 1971. "Finding the K Shortest Loopless Paths in a Network," Management Science, INFORMS, vol. 17(11), pages 712-716, July.
    5. Patrice Marcotte & Laura Wynter, 2004. "A New Look at the Multiclass Network Equilibrium Problem," Transportation Science, INFORMS, vol. 38(3), pages 282-292, August.
    6. Long, Jiancheng & Tan, Weimin & Szeto, W.Y. & Li, Yao, 2018. "Ride-sharing with travel time uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 143-171.
    7. Xu, Huayu & Pang, Jong-Shi & Ordóñez, Fernando & Dessouky, Maged, 2015. "Complementarity models for traffic equilibrium with ridesharing," Transportation Research Part B: Methodological, Elsevier, vol. 81(P1), pages 161-182.
    8. Kostas Bimpikis & Ozan Candogan & Daniela Saban, 2019. "Spatial Pricing in Ride-Sharing Networks," Operations Research, INFORMS, vol. 67(3), pages 744-769, May.
    9. Bao, Yue & Xiao, Feng & Gao, Zaihan & Gao, Ziyou, 2017. "Investigation of the traffic congestion during public holiday and the impact of the toll-exemption policy," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 58-81.
    10. Hall, Jonathan D. & Palsson, Craig & Price, Joseph, 2018. "Is Uber a substitute or complement for public transit?," Journal of Urban Economics, Elsevier, vol. 108(C), pages 36-50.
    11. Ma, Rui & Zhang, H.M., 2017. "The morning commute problem with ridesharing and dynamic parking charges," Transportation Research Part B: Methodological, Elsevier, vol. 106(C), pages 345-374.
    12. Xiao, Feng & Shen, Wei & Michael Zhang, H., 2012. "The morning commute under flat toll and tactical waiting," Transportation Research Part B: Methodological, Elsevier, vol. 46(10), pages 1346-1359.
    13. Wang, Jing-Peng & Ban, Xuegang (Jeff) & Huang, Hai-Jun, 2019. "Dynamic ridesharing with variable-ratio charging-compensation scheme for morning commute," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 390-415.
    14. Masoud, Neda & Jayakrishnan, R., 2017. "A decomposition algorithm to solve the multi-hop Peer-to-Peer ride-matching problem," Transportation Research Part B: Methodological, Elsevier, vol. 99(C), pages 1-29.
    15. Di, Xuan & Ma, Rui & Liu, Henry X. & Ban, Xuegang (Jeff), 2018. "A link-node reformulation of ridesharing user equilibrium with network design," Transportation Research Part B: Methodological, Elsevier, vol. 112(C), pages 230-255.
    16. Stiglic, Mitja & Agatz, Niels & Savelsbergh, Martin & Gradisar, Mirko, 2015. "The benefits of meeting points in ride-sharing systems," Transportation Research Part B: Methodological, Elsevier, vol. 82(C), pages 36-53.
    17. Lee, Alan & Savelsbergh, Martin, 2015. "Dynamic ridesharing: Is there a role for dedicated drivers?," Transportation Research Part B: Methodological, Elsevier, vol. 81(P2), pages 483-497.
    18. Xu, Zhengtian & Yin, Yafeng & Zha, Liteng, 2017. "Optimal parking provision for ride-sourcing services," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 559-578.
    19. Stiglic, M. & Agatz, N.A.H. & Savelsbergh, M.W.P. & Gradisar, M., 2015. "The Benefits of Meeting Points in Ride-sharing Systems," ERIM Report Series Research in Management ERS-2015-003-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    20. (Jeff) Ban, Xuegang & Dessouky, Maged & Pang, Jong-Shi & Fan, Rong, 2019. "A general equilibrium model for transportation systems with e-hailing services and flow congestion," Transportation Research Part B: Methodological, Elsevier, vol. 129(C), pages 273-304.
    21. Yang, Hai & Huang, Hai-Jun, 2004. "The multi-class, multi-criteria traffic network equilibrium and systems optimum problem," Transportation Research Part B: Methodological, Elsevier, vol. 38(1), pages 1-15, January.
    22. Mahmoudi, Monirehalsadat & Zhou, Xuesong, 2016. "Finding optimal solutions for vehicle routing problem with pickup and delivery services with time windows: A dynamic programming approach based on state–space–time network representations," Transportation Research Part B: Methodological, Elsevier, vol. 89(C), pages 19-42.
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    6. 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.
    7. Fu, Hao & Lam, William H.K. & Shao, Hu & Ma, Wei & Chen, Bi Yu & Ho, H.W., 2022. "Optimization of multi-type sensor locations for simultaneous estimation of origin-destination demands and link travel times with covariance effects," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 19-47.
    8. Yi Cao & Shan Wang & Jinyang Li, 2021. "The Optimization Model of Ride-Sharing Route for Ride Hailing Considering Both System Optimization and User Fairness," Sustainability, MDPI, vol. 13(2), pages 1-17, January.
    9. Wang, Jianbiao & Miwa, Tomio & Morikawa, Takayuki, 2023. "Recursive decomposition probability model for demand estimation of street-hailing taxis utilizing GPS trajectory data," Transportation Research Part B: Methodological, Elsevier, vol. 167(C), pages 171-195.
    10. Guo, Jiaqi & Long, Jiancheng & Xu, Xiaoming & Yu, Miao & Yuan, Kai, 2022. "The vehicle routing problem of intercity ride-sharing between two cities," Transportation Research Part B: Methodological, Elsevier, vol. 158(C), pages 113-139.
    11. 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.
    12. Noruzoliaee, Mohamadhossein & Zou, Bo, 2022. "One-to-many matching and section-based formulation of autonomous ridesharing equilibrium," Transportation Research Part B: Methodological, Elsevier, vol. 155(C), pages 72-100.
    13. Li, Tongfei & Xu, Min & Sun, Huijun & Xiong, Jie & Dou, Xueping, 2023. "Stochastic ridesharing equilibrium problem with compensation optimization," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 170(C).
    14. Wen Yi & Robyn Phipps & Hans Wang, 2020. "Sustainable Ship Loading Planning for Prefabricated Products in the Construction Industry," Sustainability, MDPI, vol. 12(21), pages 1-12, October.
    15. 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).
    16. Tang, Zhe-Yi & Tian, Li-Jun & Wang, David Z.W., 2021. "Multi-modal morning commute with endogenous shared autonomous vehicle penetration considering parking space constraint," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 151(C).

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