Joint Pricing and Matching for City-Scale Ride-Pooling
Keywords:Ride Sharing, Auctions, Mixed Integer Linear Programming, Planning And Scheduling
AbstractCentral 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 optimisation 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.
How to Cite
Shah, S., Lowalekar, M., & Varakantham, P. (2022). Joint Pricing and Matching for City-Scale Ride-Pooling. Proceedings of the International Conference on Automated Planning and Scheduling, 32(1), 499-507. https://doi.org/10.1609/icaps.v32i1.19836
Industry and Applications Track