Abstract
We study revenue-optimal pricing and driver compensation in ridesharing platforms when drivers
have heterogeneous preferences over locations. If
a platform ignores drivers’ location preferences,
it may make inefficient trip dispatches; moreover,
drivers may strategize so as to route towards their
preferred locations. In a model with stationary and
continuous demand and supply, we present a mechanism that incentivizes drivers to both (i) report
their location preferences truthfully and (ii) always
provide service. In settings with unconstrained
driver supply or symmetric demand patterns, our
mechanism achieves the full-information, first-best
revenue. Under supply constraints and unbalanced
demand, we show via simulation that our mechanism improves over existing mechanisms and has
performance close to the first-best