IDEAS home Printed from https://ideas.repec.org/a/inm/orinte/v51y2021i5p373-390.html
   My bibliography  Save this article

Driver Positioning and Incentive Budgeting with an Escrow Mechanism for Ride-Sharing Platforms

Author

Listed:
  • Hao Yi Ong

    (Lyft Inc., San Francisco, California 94107)

  • Daniel Freund

    (Sloan School of Management, MIT, Cambridge, Massachusetts 02142)

  • Davide Crapis

    (Lyft Inc., San Francisco, California 94107)

Abstract

Drivers on the Lyft ride-share platform do not always know where the areas of supply shortage are in real time. This lack of information hurts both riders trying to find a ride and drivers trying to determine how to maximize their earnings opportunities. Lyft’s Personal Power Zone (PPZ) product helps the company to maintain high levels of service on the platform by influencing the spatial distribution of drivers in real time via monetary incentives that encourage them to reposition their vehicles. The underlying system that powers the product has two main components: (1) a novel “escrow mechanism” that tracks available incentive budgets tied to locations within a city in real time, and (2) an algorithm that solves the stochastic driver-positioning problem to maximize short-run revenue from riders’ fares. The optimization problem is a multiagent dynamic program that is too complicated to solve optimally for our large-scale application. Our approach is to decompose it into two subproblems. The first determines the set of drivers to incentivize and where to incentivize them to position themselves. The second determines how to fund each incentive using the escrow budget. By formulating it as two convex programs, we are able to use commercial solvers that find the optimal solution in a matter of seconds. Rolled out to all 320 cities in which Lyft operates in a little more than a year, the system now generates millions of bonuses that incentivize hundreds of thousands of active drivers to optimally position themselves in anticipation of ride requests every week. Together, the PPZ product and its underlying algorithms represent a paradigm shift in how Lyft drivers drive and generate earnings on the platform. Its direct business impact has been a 0.5% increase in incremental bookings, amounting to tens of millions of dollars per year. In addition, the product has brought about significant improvements to the driver and rider experience on the platform. These include statistically significant reductions in pick-up times and ride cancellations. Finally, internal surveys reveal that the vast majority of drivers prefer PPZs over the legacy system.

Suggested Citation

  • Hao Yi Ong & Daniel Freund & Davide Crapis, 2021. "Driver Positioning and Incentive Budgeting with an Escrow Mechanism for Ride-Sharing Platforms," Interfaces, INFORMS, vol. 51(5), pages 373-390, September.
    • Handle: RePEc:inm:orinte:v:51:y:2021:i:5:p:373-390
    DOI: 10.1287/inte.2021.1091
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/inte.2021.1091
    Download Restriction: no
    File URL: https://libkey.io/10.1287/inte.2021.1091?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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. Bimpikis, Kostas & Candogan, Ozan & Saban, Daniela, 2016. "Spatial Pricing in Ride-Sharing Networks," Research Papers 3482, Stanford University, Graduate School of Business.
    3. Anton Braverman & J. G. Dai & Xin Liu & Lei Ying, 2019. "Empty-Car Routing in Ridesharing Systems," Operations Research, INFORMS, vol. 67(5), pages 1437-1452, September.
    4. Omar Besbes & Francisco Castro & Ilan Lobel, 2021. "Surge Pricing and Its Spatial Supply Response," Management Science, INFORMS, vol. 67(3), pages 1350-1367, March.
    5. Nikhil Garg & Hamid Nazerzadeh, 2019. "Driver Surge Pricing," Papers 1905.07544, arXiv.org, revised Mar 2021.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Alex Chin & Zhiwei Qin, 2023. "A Unified Representation Framework for Rideshare Marketplace Equilibrium and Efficiency," Papers 2302.14358, arXiv.org.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. De Munck, Thomas & Chevalier, Philippe & Tancrez, Jean-Sébastien, 2023. "Managing priorities on on-demand service platforms with waiting time differentiation," International Journal of Production Economics, Elsevier, vol. 266(C).
    3. 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.
    4. 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.
    5. Zhu, Zheng & Ke, Jintao & Wang, Hai, 2021. "A mean-field Markov decision process model for spatial-temporal subsidies in ride-sourcing markets," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 540-565.
    6. 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.
    7. Saif Benjaafar & Ming Hu, 2020. "Operations Management in the Age of the Sharing Economy: What Is Old and What Is New?," Manufacturing & Service Operations Management, INFORMS, vol. 22(1), pages 93-101, January.
    8. Saif Benjaafar & Shining Wu & Hanlin Liu & Einar Bjarki Gunnarsson, 2022. "Dimensioning On-Demand Vehicle Sharing Systems," Management Science, INFORMS, vol. 68(2), pages 1218-1232, February.
    9. Amirmahdi Tafreshian & Neda Masoud & Yafeng Yin, 2020. "Frontiers in Service Science: Ride Matching for Peer-to-Peer Ride Sharing: A Review and Future Directions," Service Science, INFORMS, vol. 12(2-3), pages 44-60, June.
    10. Nikhil Garg & Hamid Nazerzadeh, 2022. "Driver Surge Pricing," Management Science, INFORMS, vol. 68(5), pages 3219-3235, May.
    11. 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.
    12. Yunke Mai & Bin Hu & Saša Pekeč, 2023. "Courteous or Crude? Managing User Conduct to Improve On-Demand Service Platform Performance," Management Science, INFORMS, vol. 69(2), pages 996-1016, February.
    13. Zhang, Kenan & Nie, Yu (Marco), 2021. "To pool or not to pool: Equilibrium, pricing and regulation," Transportation Research Part B: Methodological, Elsevier, vol. 151(C), pages 59-90.
    14. Zhang, Kenan & Nie, Yu (Marco), 2022. "Mitigating traffic congestion induced by transportation network companies: A policy analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 96-118.
    15. 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.
    16. Guo, Xiaotong & Caros, Nicholas S. & Zhao, Jinhua, 2021. "Robust matching-integrated vehicle rebalancing in ride-hailing system with uncertain demand," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 161-189.
    17. Chenglong Zhang & Jianqing Chen & Srinivasan Raghunathan, 2022. "Two-Sided Platform Competition in a Sharing Economy," Management Science, INFORMS, vol. 68(12), pages 8909-8932, December.
    18. 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).
    19. Long Gao & Jim (Junmin) Shi & Michael F. Gorman & Ting Luo, 2020. "Business Analytics for Intermodal Capacity Management," Manufacturing & Service Operations Management, INFORMS, vol. 22(2), pages 310-329, March.
    20. Gérard P. Cachon, 2020. "A Research Framework for Business Models: What Is Common Among Fast Fashion, E-Tailing, and Ride Sharing?," Management Science, INFORMS, vol. 66(3), pages 1172-1192, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:orinte:v:51:y:2021:i:5:p:373-390. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.