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Spatial Pricing in Ride-Sharing Networks

Author

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  • Bimpikis, Kostas

    (Stanford University)

  • Candogan, Ozan

    (Chicago University)

  • Saban, Daniela

    (Stanford University)

Abstract

We explore spatial price discrimination in the context of a ride-sharing platform that serves a network of locations. Riders at different locations are heterogeneous in terms of their destination preferences, as captured by the demand pattern of the underlying network. Drivers decide whether, when, and where to provide service so as to maximize their expected earnings given the platform's pricing policy. Our findings highlight the impact of the demand pattern of the underlying network on the platform's optimal profits and aggregate consumer surplus. In particular, we establish that both profits and consumer surplus are maximized when the demand pattern is "balanced" across the network's locations. In addition, we show that profits and consumer surplus are monotonic with the balancedness of the demand pattern (as formalized by the pattern's structural properties). Furthermore, we explore the widely adopted compensation scheme that allocates a constant fraction of the fare to drivers and identify a class of networks for which it can implement the optimal equilibrium outcome. However, we also showcase that generally this scheme leads to significantly lower profits for the platform than the optimal pricing policy especially in the presence of heterogeneity among the demand patterns in different locations. Together, these results illustrate the value of accounting for the demand pattern across a network's locations when designing the platform's pricing policy, and complement the existing focus on the benefits of dynamic (surge) pricing to deal with demand fluctuations over time.

Suggested Citation

  • Bimpikis, Kostas & Candogan, Ozan & Saban, Daniela, 2016. "Spatial Pricing in Ride-Sharing Networks," Research Papers 3482, Stanford University, Graduate School of Business.
  • Handle: RePEc:ecl:stabus:3482
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    Cited by:

    1. Hao Yi Ong & Daniel Freund & Davide Crapis, 2021. "Driver Positioning and Incentive Budgeting with an Escrow Mechanism for Ridesharing Platforms," Papers 2104.14740, arXiv.org.
    2. Oliveira, Beatriz B. & Carravilla, Maria Antónia & Oliveira, José F. & Costa, Alysson M., 2019. "A co-evolutionary matheuristic for the car rental capacity-pricing stochastic problem," European Journal of Operational Research, Elsevier, vol. 276(2), pages 637-655.
    3. Lin, Xiaogang & Sun, Cuiying & Cao, Bin & Zhou, Yong-Wu & Chen, Chuanying, 2021. "Should ride-sharing platforms cooperate with car-rental companies? Implications for consumer surplus and driver surplus," Omega, Elsevier, vol. 102(C).
    4. Soheil Ghili, 2021. "Optimal Bundling: Characterization, Interpretation, and Implications for Empirical Work," Cowles Foundation Discussion Papers 2273, Cowles Foundation for Research in Economics, Yale University.
    5. Sun, Luoyi & Teunter, Ruud H. & Hua, Guowei & Wu, Tian, 2020. "Taxi-hailing platforms: Inform or Assign drivers?," Transportation Research Part B: Methodological, Elsevier, vol. 142(C), pages 197-212.
    6. Soheil Ghili & Vineet Kumar, 2020. "Spatial Distribution of Supply and the Role of Market Thickness: Theory and Evidence from Ride Sharing," Cowles Foundation Discussion Papers 2219, Cowles Foundation for Research in Economics, Yale University.
    7. Soheil Ghili & Vineet Kumar, 2020. "Spatial Distribution of Supply and the Role of Market Thickness: Theory and Evidence from Ride Sharing," Cowles Foundation Discussion Papers 2219R, Cowles Foundation for Research in Economics, Yale University, revised Aug 2020.
    8. Zhou, Yong-Wu & Lin, Xiaogang & Zhong, Yuanguang & Xie, Wei, 2019. "Contract selection for a multi-service sharing platform with self-scheduling capacity," Omega, Elsevier, vol. 86(C), pages 198-217.
    9. Daozhi Zhao & Mingyang Chen, 2019. "Ex-ante versus ex-post destination information model for on-demand service ride-sharing platform," Annals of Operations Research, Springer, vol. 279(1), pages 301-341, August.
    10. 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.

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