Joint Pricing and Matching for City-Scale Ride-Pooling

Abstract

Central to efficient ride-pooling are two challenges: (1) how to 'price' customers' requests for rides, and (2) if the customer agrees to that price, how to best 'match' these requests to drivers. While both of them are interdependent, each challenge's individual complexity has meant that, historically, they have been decoupled and studied individually. This paper creates a framework for batched pricing and matching in which pricing is seen as a meta-level optimization over different possible matching decisions. Our key contributions are in developing a variant of the revenue-maximizing auction corresponding to the meta-level optimization problem, and then providing a scalable mechanism for computing posted prices. We test our algorithm on real-world data at city-scale and show that our algorithm reliably matches demand to supply across a range of parameters.