Spatio-temporal pricing for ridesharing platforms

Hongyao Ma, Fei Fang, David C. Parkes

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 will choose to accept their dispatched trips, rather than drive to another area or wait for higher prices or a better trip. We work in a complete information, discrete time, multi-period, multi-location model, and 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. The mechanism is also welfare-optimal, envy-free, individually rational, budget balanced and core-selecting in equilibrium from any history onward. The proof of incentive alignment makes use of the M♯ concavity of minimum cost flow objectives. We also give an impossibility result, that there can be no dominant-strategy mechanism with the same economic properties. Simulation results suggest that the STP mechanism can achieve significantly higher social welfare than a myopic pricing mechanism.