Joint pricing and matching for city-scale ride pooling

• Main Authors: SHAH, Sanket, LOWALEKAR, Meghna, VARAKANTHAM, Pradeep
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Ride Sharing; Auctions; Mixed Integer Linear Programming; Planning And Scheduling; Information Security
• Online Access: https://ink.library.smu.edu.sg/sis_research/7656; https://ink.library.smu.edu.sg/context/sis_research/article/8659/viewcontent/Joint.pdf
• Institution: Singapore Management University

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.