Driver Surge Pricing

Nikhil Garg () and Hamid Nazerzadeh ()
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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

Management Science, 2022, vol. 68, issue 5, 3219-3235

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.

Keywords: dynamic programming; applications; inventory production; policies; pricing; transportation: models (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (5)

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