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Path-based dynamic pricing for vehicle allocation in ridesharing systems with fully compliant drivers

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  • Lei, Chao
  • Jiang, Zhoutong
  • Ouyang, Yanfeng

Abstract

Rapidly advancing on-demand ridesharing services, including those with self-driving technologies, hold the promise to revolutionize delivery of mobility. Yet, significant imbalance between spatiotemporal distributions of vehicle supply and travel demand poses a pressing challenge. This paper proposes a multi-period game-theoretic model that addresses dynamic pricing and idling vehicle dispatching problems in the on-demand ridesharing systems with fully compliant drivers/vehicles. A dynamic mathematical program with equilibrium constraints (MPEC) is formulated to capture the interdependent decision-making processes of the mobility service provider (e.g., regarding vehicle allocation) and travelers (e.g., regarding ride-sharing and travel path options). An algorithm based on approximate dynamic programming (ADP), with customized subroutines for solving the MPEC, is developed to solve the overall problem. It is shown with numerical experiments that the proposed dynamic pricing and vehicle dispatching strategy can help ridesharing service providers achieve better system performance (as compared with myopic policies) while facing spatial and temporal variations in ridesharing demand.

Suggested Citation

  • Lei, Chao & Jiang, Zhoutong & Ouyang, Yanfeng, 2020. "Path-based dynamic pricing for vehicle allocation in ridesharing systems with fully compliant drivers," Transportation Research Part B: Methodological, Elsevier, vol. 132(C), pages 60-75.
  • Handle: RePEc:eee:transb:v:132:y:2020:i:c:p:60-75
    DOI: 10.1016/j.trb.2019.01.017
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    1. Gérard P. Cachon & Kaitlin M. Daniels & Ruben Lobel, 2017. "The Role of Surge Pricing on a Service Platform with Self-Scheduling Capacity," Manufacturing & Service Operations Management, INFORMS, vol. 19(3), pages 368-384, July.
    2. Bai, Yun & Ouyang, Yanfeng & Pang, Jong-Shi, 2016. "Enhanced models and improved solution for competitive biofuel supply chain design under land use constraints," European Journal of Operational Research, Elsevier, vol. 249(1), pages 281-297.
    3. An, Shi & Cui, Na & Bai, Yun & Xie, Weijun & Chen, Mingliu & Ouyang, Yanfeng, 2015. "Reliable emergency service facility location under facility disruption, en-route congestion and in-facility queuing," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 82(C), pages 199-216.
    4. Yu, Biying & Ma, Ye & Xue, Meimei & Tang, Baojun & Wang, Bin & Yan, Jinyue & Wei, Yi-Ming, 2017. "Environmental benefits from ridesharing: A case of Beijing," Applied Energy, Elsevier, vol. 191(C), pages 141-152.
    5. Bimpikis, Kostas & Candogan, Ozan & Saban, Daniela, 2016. "Spatial Pricing in Ride-Sharing Networks," Research Papers 3482, Stanford University, Graduate School of Business.
    6. Daniel J. Fagnant & Kara M. Kockelman, 2018. "Dynamic ride-sharing and fleet sizing for a system of shared autonomous vehicles in Austin, Texas," Transportation, Springer, vol. 45(1), pages 143-158, January.
    7. Meyer, Ina & Kaniovski, Serguei & Scheffran, Jürgen, 2012. "Scenarios for regional passenger car fleets and their CO2 emissions," Energy Policy, Elsevier, vol. 41(C), pages 66-74.
    8. Gabriel, Steven A. & Leuthold, Florian U., 2010. "Solving discretely-constrained MPEC problems with applications in electric power markets," Energy Economics, Elsevier, vol. 32(1), pages 3-14, January.
    9. Agatz, Niels A.H. & Erera, Alan L. & Savelsbergh, Martin W.P. & Wang, Xing, 2011. "Dynamic ride-sharing: A simulation study in metro Atlanta," Transportation Research Part B: Methodological, Elsevier, vol. 45(9), pages 1450-1464.
    10. Warren Powell & Andrzej Ruszczyński & Huseyin Topaloglu, 2004. "Learning Algorithms for Separable Approximations of Discrete Stochastic Optimization Problems," Mathematics of Operations Research, INFORMS, vol. 29(4), pages 814-836, November.
    11. Xie, Weijun & Ouyang, Yanfeng, 2015. "Optimal layout of transshipment facility locations on an infinite homogeneous plane," Transportation Research Part B: Methodological, Elsevier, vol. 75(C), pages 74-88.
    12. Jing Hu & John Mitchell & Jong-Shi Pang & Bin Yu, 2012. "On linear programs with linear complementarity constraints," Journal of Global Optimization, Springer, vol. 53(1), pages 29-51, May.
    13. 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.
    14. Hosni, Hadi & Naoum-Sawaya, Joe & Artail, Hassan, 2014. "The shared-taxi problem: Formulation and solution methods," Transportation Research Part B: Methodological, Elsevier, vol. 70(C), pages 303-318.
    15. Yang, Hai & Bell, Michael G. H., 2001. "Transport bilevel programming problems: recent methodological advances," Transportation Research Part B: Methodological, Elsevier, vol. 35(1), pages 1-4, January.
    16. Furuhata, Masabumi & Dessouky, Maged & Ordóñez, Fernando & Brunet, Marc-Etienne & Wang, Xiaoqing & Koenig, Sven, 2013. "Ridesharing: The state-of-the-art and future directions," Transportation Research Part B: Methodological, Elsevier, vol. 57(C), pages 28-46.
    17. Daganzo, Carlos F., 1994. "The cell transmission model: A dynamic representation of highway traffic consistent with the hydrodynamic theory," Transportation Research Part B: Methodological, Elsevier, vol. 28(4), pages 269-287, August.
    18. D'Acierno, Luca & Gallo, Mariano & Montella, Bruno, 2006. "Optimisation models for the urban parking pricing problem," Transport Policy, Elsevier, vol. 13(1), pages 34-48, January.
    19. Agatz, Niels & Erera, Alan & Savelsbergh, Martin & Wang, Xing, 2012. "Optimization for dynamic ride-sharing: A review," European Journal of Operational Research, Elsevier, vol. 223(2), pages 295-303.
    20. 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.
    21. Cortés, Cristián E. & Matamala, Martín & Contardo, Claudio, 2010. "The pickup and delivery problem with transfers: Formulation and a branch-and-cut solution method," European Journal of Operational Research, Elsevier, vol. 200(3), pages 711-724, February.
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    Cited by:

    1. Yining Liu & Yanfeng Ouyang, 2022. "Planning ride-pooling services with detour restrictions for spatially heterogeneous demand: A multi-zone queuing network approach," Papers 2208.02219, arXiv.org, revised Jun 2023.
    2. Lei, Chao & Ouyang, Yanfeng, 2024. "Average minimum distance to visit a subset of random points in a compact region," Transportation Research Part B: Methodological, Elsevier, vol. 181(C).
    3. Chen, Xiqun (Michael) & Zheng, Hongyu & Ke, Jintao & Yang, Hai, 2020. "Dynamic optimization strategies for on-demand ride services platform: Surge pricing, commission rate, and incentives," Transportation Research Part B: Methodological, Elsevier, vol. 138(C), pages 23-45.
    4. Liu, Yining & Ouyang, Yanfeng, 2023. "Planning ride-pooling services with detour restrictions for spatially heterogeneous demand: A multi-zone queuing network approach," Transportation Research Part B: Methodological, Elsevier, vol. 174(C).
    5. Si, Jinhua & He, Fang & Lin, Xi & Tang, Xindi, 2024. "Vehicle dispatching and routing of on-demand intercity ride-pooling services: A multi-agent hierarchical reinforcement learning approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 186(C).
    6. Ouyang, Yanfeng & Yang, Haolin, 2023. "Measurement and mitigation of the “wild goose chase” phenomenon in taxi services," Transportation Research Part B: Methodological, Elsevier, vol. 167(C), pages 217-234.
    7. Xi, Haoning & Aussel, Didier & Liu, Wei & Waller, S.Travis. & Rey, David, 2024. "Single-leader multi-follower games for the regulation of two-sided mobility-as-a-service markets," European Journal of Operational Research, Elsevier, vol. 317(3), pages 718-736.
    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. Rajendran, Suchithra & Srinivas, Sharan, 2020. "Air taxi service for urban mobility: A critical review of recent developments, future challenges, and opportunities," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 143(C).
    10. Daganzo, Carlos F. & Ouyang, Yanfeng & Yang, Haolin, 2020. "Analysis of ride-sharing with service time and detour guarantees," Transportation Research Part B: Methodological, Elsevier, vol. 140(C), pages 130-150.
    11. Meijian Yang & Enjun Xia, 2021. "A Systematic Literature Review on Pricing Strategies in the Sharing Economy," Sustainability, MDPI, vol. 13(17), pages 1-28, August.

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