Satisficing Approach to On-Demand Ride Matching

Abstract

Online ride-hailing platforms have developed into an integral part of the transportation infrastructure in many countries. The primary task of a ride-hailing platform is to match trip requests to drivers in real time. Although both passengers and drivers prefer a prompt pickup to initiate the trips, it is often difficult to find a nearby driver for every passenger. If the driver is far from the pickup point, the passenger may cancel the trip while the driver is heading toward the pickup point. For the platform to be profitable, the trip cancellation rate must be maintained at a low level. We propose a computationally efficient data-driven approach to ride matching, in which a pickup time target is imposed on each trip request and an optimization problem is formulated to maximize the joint probability of all the pickup times meeting the targets. By adjusting pickup time targets individually, this approach may assign more high-value trip requests to nearby drivers, thus boosting the platform’s revenue while maintaining a low cancellation rate. In numerical experiments, the proposed approach outperforms several ride-matching policies used in practice. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms—Discrete. Funding: This work of D. Rong and X. Sun was supported in part by the National Natural Science Foundation of China [Grant 71971165], the National Key Research and Development Program of China [Grant 2021YFB3301801], the MOE Project of Humanities and Social Science of China [Grant 19YJE630002], and the Soft Science Research Program of Shannxi [Grant 2018KRZ005]. The work of S. He was supported in part by the Singapore Ministry of Education Social Science Research Council [Grant MOE2022-SSRTG-029]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0210 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2021.0210 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .