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Driver Surge Pricing

Author

Listed:
  • Nikhil Garg

    (Operations Research and Information Engineering, Cornell Tech and Technion, New York, New York 10044)

  • Hamid Nazerzadeh

    (Marshall Business School, University of Southern California, Los Angeles, California 90089; Uber Technologies, San Francisco, California 94103)

Abstract

Ride-hailing marketplaces like Uber and Lyft use dynamic pricing, often called surge, to balance the supply of available drivers with the demand for rides. We study driver-side payment mechanisms for such marketplaces, presenting the theoretical foundation that has informed the design of Uber’s new additive driver surge mechanism. We present a dynamic stochastic model to capture the impact of surge pricing on driver earnings and their strategies to maximize such earnings. In this setting, some time periods (surge) are more valuable than others (nonsurge), and therefore trips of different time lengths vary in the induced driver opportunity cost. First, we show that multiplicative surge, historically the standard on ride-hailing platforms, is not incentive compatible in a dynamic setting. We then propose a structured, incentive-compatible pricing mechanism. This closed-form mechanism has a simple form and is well approximated by Uber’s new additive surge mechanism. Finally, through both numerical analysis and real data from a ride-hailing marketplace, we show that additive surge is more incentive compatible in practice than is multiplicative surge.

Suggested Citation

  • Nikhil Garg & Hamid Nazerzadeh, 2022. "Driver Surge Pricing," Management Science, INFORMS, vol. 68(5), pages 3219-3235, May.
    • Handle: RePEc:inm:ormnsc:v:68:y:2022:i:5:p:3219-3235
    DOI: 10.1287/mnsc.2021.4058
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    References listed on IDEAS

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    Cited by:

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    3. Jiaru Bai & H. Sebastian Heese & Manish Tripathy, 2023. "Hiding in plain sight: Surge pricing and strategic providers," Production and Operations Management, Production and Operations Management Society, vol. 32(12), pages 3837-3855, December.
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    6. Hongseok Namkoong & Yuanzhe Ma & Peter W. Glynn, 2026. "Minimax Optimal Estimation of Stability Under Distribution Shift," Operations Research, INFORMS, vol. 74(1), pages 464-483, January.
    7. Jiayang Li & Guoyin Zhang & Debing Ni, 2025. "Drivers’ Welfare and Pollutant Emission Induced by Ride-Hailing Platforms’ Pricing Strategies," Sustainability, MDPI, vol. 17(9), pages 1-35, April.
    8. Saif Benjaafar & Zicheng Wang & Xiaotang Yang, 2025. "The Impact of Automation on Workers When Workers Are Strategic: The Case of Ride-Hailing," Manufacturing & Service Operations Management, INFORMS, vol. 27(5), pages 1571-1586, September.
    9. Cai, Zeen & Mo, Dong & Tang, Wei & Chen, Yong & Chen, Xiqun (Michael), 2023. "A two-period game-theoretical model for heterogeneous ride-sourcing platforms with asymmetric competition and mixed fleets," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 178(C).
    10. Xu, Yu & Ling, Liuyi & Wu, Jie & Xu, Shengshuo, 2024. "On-demand ride-hailing platforms under green mobility: Pricing strategies and government regulation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 189(C).
    11. Juan Camilo Castillo & Dan Knoepfle & E. Glen Weyl, 2025. "Matching and Pricing in Ride Hailing: Wild Goose Chases and How to Solve Them," Management Science, INFORMS, vol. 71(5), pages 4377-4395, May.
    12. Yeung, Timothy Yu-Cheong & Zhu, Dianzhuo, 2022. "Intercity ridesharing to the rescue: Capacity flexibility and price stability of BlaBlaCar during the 2018 French railway strike," Transportation Research Part A: Policy and Practice, Elsevier, vol. 164(C), pages 270-290.
    13. Julia Yan & Sébastien Martin & Sean J. Taylor, 2025. "Trading Flexibility for Adoption: From Dynamic to Static Walking in Ride-Sharing," Management Science, INFORMS, vol. 71(7), pages 5875-5892, July.
    14. Yu Jeffrey Hu & Jeroen Rombouts & Ines Wilms, 2023. "Fast Forecasting of Unstable Data Streams for On-Demand Service Platforms," Papers 2303.01887, arXiv.org, revised May 2024.
    15. Song, Bing & Jian, Sisi, 2025. "Balancing privacy and revenue: A differentially private dynamic pricing algorithm for ride-hailing," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 203(C).
    16. Gao, Chang & Wang, Yineng & He, Fang & Lin, Xi, 2025. "Modeling and analyzing ride-hailing market equilibrium considering drivers’ multi-homing choice behavior," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 197(C).
    17. Koley, Souradeep & Kumar Barua, Mukesh & Bisi, Arnab, 2025. "Inhibitors in ridesharing firms from developing Nations: A novel Integrated MCDM – Text Mining approach using Large-Scale data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 193(C).

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