Joint Pricing and Matching for City-Scale Ride-Pooling

Authors

  • Sanket Shah Harvard University
  • Meghna Lowalekar Singapore Management University
  • Pradeep Varakantham Singapore Management University

Keywords:

Ride Sharing, Auctions, Mixed Integer Linear Programming, Planning And Scheduling

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 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.

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Published

2022-06-13

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. Retrieved from https://ojs.aaai.org/index.php/ICAPS/article/view/19836