Spatio-Temporal Pricing for Ridesharing Platforms

Hongyao Ma (), Fei Fang () and David C. Parkes ()
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Hongyao Ma: Decision, Risk, and Operations Division, Columbia Business School, New York, New York 10027
Fei Fang: School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
David C. Parkes: John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, Massachusetts 02134

Operations Research, 2022, vol. 70, issue 2, 1025-1041

Abstract: Ridesharing platforms match drivers and riders to trips, using dynamic prices to balance supply and demand. A challenge is to set prices that are appropriately smooth in space and time, so that drivers with the flexibility to decide how to work will nevertheless choose to accept their dispatched trips rather than drive to another area or wait for higher prices or a better trip. In this work, we propose a complete information model that is simple yet rich enough to incorporate spatial imbalance and temporal variations in supply and demand—conditions that lead to market failures in today’s platforms. We introduce the spatio-temporal pricing (STP) mechanism . The mechanism is incentive-aligned, in that it is a subgame-perfect equilibrium for drivers to always accept their trip dispatches. From any history onward, the equilibrium outcome of the STP mechanism is welfare-optimal, envy-free, individually rational, budget balanced, and core-selecting. We also prove the impossibility of achieving the same economic properties in a dominant-strategy equilibrium. Simulation results show that the STP mechanism can achieve substantially improved social welfare and earning equity than a myopic mechanism.

Keywords: Market Analytics and Revenue Management; market design; ridesharing; incentive compatibility (search for similar items in EconPapers)
Date: 2022
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