Developing an Optimal Peer-to-Peer Ride-Matching Problem Algorithm with Ride Transfers

Abstract

Thanks to recent developments in ride-hailing transit services throughout the world, the peer-to-peer (P2P) ride-matching problem has been actively considered in academia in recent years. P2P ride-matching not only reduces travel costs for riders but also benefits drivers by saving them money in exchange for their additional travel time. However, assigning riders to drivers in an efficient way is a complex problem that requires focusing on maximizing the benefits for both riders and drivers. This study aims to formulate a multi-driver multi-rider (MDMR) P2P ride-matching problem, based on rational preferences and cost allocation for both driver and rider, which also allows riders to transfer between multiple drivers for their travels if needed. Tabu Search (TS) for system-optimal ride-matching and a Greedy Matching (GM) algorithm for stable ride-matching were developed to solve ride-matching cases. The results show that the developed algorithm can successfully solve the proposed P2P MDMR ride-matching problem. MDMR P2P ride-matching can be used in areas where not much demand for ride-sharing exists, or for long-distance travel. Also it can be applied to designs for a more efficient on-demand transit network which can allow for transfers between routes. Moreover, the comparison of results between two implemented approaches shows that system-optimal centralized ride-matching can bring more cost savings for all participants in the system, although it may not always be stable when riders and drivers can choose their ride-matching for their own maximum benefit.