Frontiers in Service Science: Ride Matching for Peer-to-Peer Ride Sharing: A Review and Future Directions

Abstract

As a consequence of the sharing economy attaining more popularity, there has been a shift toward shared-use mobility services in recent years, especially those that encourage users to share their personal vehicles with others. To date, different variants of these services have been proposed that call for different settings and give rise to different research questions. Peer-to-peer (P2P) ride sharing is one such service that provides a platform for drivers to share their personal trips with riders who have similar itineraries. Unlike ride-sourcing services, drivers in P2P ride sharing have their own individual trips to make and are not driving for the sole purpose of serving rider requests. Unlike traditional carpooling, P2P ride sharing can serve on-demand and one-time trip requests. P2P ride sharing has been identified as a sustainable mode of transportation that results in several individual and societal benefits. The core of a P2P ride-sharing system is a ride-matching problem that determines ride-sharing plans for users. This paper reviews the major studies on the operations of P2P ride-sharing systems, with a focus on modeling and solution methodologies for matching, routing, and scheduling. In this paper, we classify ride-sharing systems based on their operational features and review the existing methodologies for each class. We further discuss a number of important directions for future research.

A Ridesharing Choice Behavioral Equilibrium Model with Users of Heterogeneous Values of Time

“…In recent years, due to the prosperity of sharing economic, a new breakthrough has been found to deal with above tricky issues. The shared mobility, one of the most important business models in the sharing economic, has many forms, including carsharing, ridesourcing, carpooling, taxisharing, bikesharing, scooter sharing, and ridesharing [4]. Among these shared mobility services, ridesharing in particular can substantially reduce the number of single-occupancy vehicles on roads, which may ameliorate the perennial traffic congestion, as well as reduce energy consumption [5][6][7].…”

Multi-Objective Linear Optimization Problem for Strategic Planning of Shared Autonomous Vehicle Operation and Infrastructure Design

“…Application of automated, connected, and/or ridesharing vehicles to intelligent transportation systems has been extensively studied in the recent literature. For comprehensive and detailed reviews, readers may refer to [2], [22]- [24].…”

“…SAV systems may able to do more efficient routing and passenger pickup and drop-off because of the automated driving and cooperation among SAVs. One of the most popular approaches is mixed integer linear programming (MILP) on time-expanded networks [8], [24], in which passengers and SAVs are dynamically matched. Furthermore, ridesharing can be directly incorporated to the MILP framework [9], [10], [24], [25].…”

“…One of the most popular approaches is mixed integer linear programming (MILP) on time-expanded networks [8], [24], in which passengers and SAVs are dynamically matched. Furthermore, ridesharing can be directly incorporated to the MILP framework [9], [10], [24], [25]. The objective functions of such MILPs are commonly a systemwide performance index such as total travel time and vehicle distance traveled; this implicitly or explicitly assumes that the SAV systems are operated by a central operator.…”

“…The objective functions of such MILPs are commonly a systemwide performance index such as total travel time and vehicle distance traveled; this implicitly or explicitly assumes that the SAV systems are operated by a central operator. Decentralized optimization has also been studied for computational efficiency or robustness against failures [24].…”