Spatial Pricing in Ride-Sharing Networks

Abstract

We explore spatial price discrimination in the context of a ride-sharing platform that serves a network of locations. Riders at different locations are heterogeneous in terms of their destination preferences, as captured by the demand pattern of the underlying network. Drivers decide whether, when, and where to provide service so as to maximize their expected earnings given the platform’s pricing policy. Our findings highlight the impact of the demand pattern of the underlying network on the platform’s optimal profits and aggregate consumer surplus. In particular, we establish that both profits and consumer surplus are maximized when the demand pattern is “balanced” across the network’s locations. In addition, we show that profits and consumer surplus are monotonic with the balancedness of the demand pattern (as formalized by the pattern’s structural properties). Furthermore, we explore the widely adopted compensation scheme that allocates a constant fraction of the fare to drivers and identify a class of networks for which it can implement the optimal equilibrium outcome. However, we also showcase that generally this scheme leads to significantly lower profits for the platform than the optimal pricing policy especially in the presence of heterogeneity among the demand patterns in different locations. Together, these results illustrate the value of accounting for the demand pattern across a network’s locations when designing the platform’s pricing policy, and complement the existing focus on the benefits of dynamic (surge) pricing to deal with demand fluctuations over time.