Spatio-Temporal Pricing for Ridesharing Platforms

Abstract

Ridesharing platforms match drivers and riders to trips, using dynamic prices to balance supply and demand. Despite having radically changed the way people get around in urban areas, there still remain a number of major challenges, undercutting their stated mission of "providing transportation as reliable as running water." A particular concern is that with the real-time flexibility to decide when and where to drive, drivers will strategize to improve their own income: 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 end in anticipation of a price increase. Many of these incentive issues are a symptom of suboptimal dispatching, and a lack of smoothness in pricing in both time and space. For example, matching drivers to trips that sends them away from a sports stadium five minutes before a game ends, and at low prices, is inefficient, and drivers are responding to a suboptimal design, and may be acting to improve efficiency.