Optimal pricing for ride-sourcing platforms

Abstract Online car hailing platforms are rapidly gaining popularity. Unlike most two-sided markets, these platforms have pricing power. The price for a specific customer ride request affects the number of interested drivers and the likelihood that a customer will accept a selected driver (and not opt for a regular taxi service). This study determines the optimal pricing strategy for online car hailing platforms, taking both ride details and driver location into account, and assuming that drivers and customers maximize utility. We do so for two types of driver selection: first to respond or closest to the customer. Under selection of the first driver to respond, we find that the platform price consists of a ride length based fare (set relative to the competing regular taxi fare) and a rush hour congestion fee, and increases with the customer waiting cost. Furthermore, the platform price is below the regular taxi fare if traffic conditions are good, drivers have low profit expectations, and the platform commission is low. We also discuss the effects on price and profit if the platform switches from first-to-respond to selecting the closest driver, which the popular Didi Chuxing platform has recently done in many Chinese cities. A numerical study based on the Beijing market further illustrates the findings.