VOMA: A Privacy-Preserving Matching Mechanism Design for Community Ride-Sharing

Abstract

Providing high-quality matching between drivers and riders is imperative for sustaining the growth of ride-sharing platforms. A user-focused matching mechanism design plays a key role in terms of ensuring user satisfaction. In this paper, we consider the matching problem in the community ride-sharing setting, where drivers and riders have strong personal preferences over the matched counterparties. Obtaining high-quality solutions that accommodate drivers’ and riders’ preferences in such a setting is particularly challenging as drivers and riders maybe reluctant to share with the platform their personal preferences over their ride-sharing counterparties due to privacy and ethical concerns. To this end, we propose a VOting-based MAtching (VOMA) mechanism to compute near-optimal matching solutions for drivers and riders, while preserving their privacy. The mechanism is a distributed implementation of the simulated annealing meta-heuristic, which computes matching solutions by guiding drivers and riders in the distributed search process using an iterative voting protocol. We evaluate the performance of VOMA using test cases generated based on New York taxi data sets. The experiment results show that the proposed matching mechanism achieves on average 90.9% efficiency compared with optimal solutions. We also show that VOMA improves the vehicle miles traveled (VMT) savings by up to 35% compared to an alternative voting-based greedy matching mechanism. System scalability and other practical issues regarding the implementation of such a matching mechanism in community ride-sharing platforms are also discussed.