Impacts of integrating shared autonomous vehicles into a Peer-to-Peer ridesharing system

• Published in: Procedia computer science Vol. 151; pp. 511 - 518
• Main Authors: An, Sunghi, Nam, Daisik, Jayakrishnan, R.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 2019
• Subjects: Fleet size; Integrated shared-mobility system; Peer-to-Peer ridesharing; Ridesharing matching rate; Shared-autonomous fleet vehicles; Ridesharing matching rate; Fleet size; Integrated shared-mobility system; Peer-to-Peer ridesharing; Shared-autonomous fleet vehicles
• ISSN: 1877-0509; 1877-0509
• DOI: 10.1016/j.procs.2019.04.069

As public perception of sharing economy in transportation has changed, mobilephone-hailed ridesharing is gaining prominence. The key aspect of capitalizing and promoting better shared-mobility systems depends on the matching rate between the supply and demand for rides. Peer-to-peer (P2P) ridesharing systems devise higher matching rate than pure ridesharing systems by attracting more drivers. Even relaxing the spatiotemporal constraints for participants could increase the chances to be matched. However, we notice that sole P2P ridesharing systems still do not guarantee matching when the number of drivers is limited. We propose the utilization of a fleet service to cover the unmatched riders in P2P ridesharing. While it can be any type of fleet services such as taxis, Uber/Lyft, or paratransit, we explore the idea of utilizing shared autonomous vehicles as a fleet, as they can be dispatched without labor. We model an integrated system for P2P ridesharing and shared autonomous fleet vehicles (SAFVs). The proposed algorithm is designed to maximize matching ratio while optimizing the number of required SAFVs. Based on a simulated study on the northern Los Angeles, the integrated shared-mobility system is shown to have high potential to serve a high fraction of riders.