Spatio-Temporal Pricing for Ridesharing Platforms

Abstract

Ridesharing platforms have radically changed the way people get around in urban areas, but there remain challenges undercutting the mission of “making transportation as reliable as running water.” A particular concern is that drivers strategize: calling riders to find out their destinations and canceling trips that are not worthwhile, declining trips and chasing surge prices in neighboring areas, and going off-line before large events will end in anticipation of a price increase. In this work, we show that such strategic behaviors are symptoms of inefficiencies in the pricing and dispatching rules governing today's platforms. We propose the Spatio-Temporal Pricing mechanism, which solves for the welfare-optimal matching of drivers to trips, and sets prices that are appropriately smooth in both space and time such that the best thing for drivers to do is accept any proposed trip dispatch. This demonstrates that ridesharing platforms can succeed in optimally orchestrating trips and providing reliable transpiration for riders, while still leaving drivers with the flexibility to decide how to work.