Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2022

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.

Keywords

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

Discipline

Information Security

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the 32nd International Conference on Automated Planning and Scheduling, Singapore, Singapore, Virtually, 2022 June 13–24

Volume

32

First Page

499

Last Page

507

Identifier

10.1609/icaps.v32i1.19836

Publisher

AAAI

City or Country

California

Additional URL

https://doi.org/10.1609/icaps.v32i1.19836

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