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Media type:
E-Article
Title:
Driver Surge Pricing
Contributor:
Garg, Nikhil;
Nazerzadeh, Hamid
Published:
Institute for Operations Research and the Management Sciences (INFORMS), 2022
Published in:
Management Science, 68 (2022) 5, Seite 3219-3235
Language:
English
DOI:
10.1287/mnsc.2021.4058
ISSN:
0025-1909;
1526-5501
Origination:
Footnote:
Description:
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. This paper was accepted by David Simchi-Levi, revenue management and market analytics.