Demand-Aware Distributed Pathfinding for Repositioning Vehicles in Shared-use Autonomous Mobility Services

Abstract

Shared-use autonomous mobility services (SAMS) have the potential to provide accessible and demand-responsive mobility to passengers, while benefitting from autonomous vehicle (AV) technology and bypassing challenges related to supply-side incentives or individual driver goals. SAMS operators typically aim to achieve efficiency and improved service quality in their fleet operations, both of which are further enabled by the use of AVs. Specifically, fleet repositioning decisions in anticipation of future demand can improve service quality, but existing approaches in the literature seldom consider the problem of routing repositioning vehicles in a way that further improves SAMS objectives. This paper presents an approach for demand-aware distributed routing pathfinding for repositioning vehicles, which can supplement existing vehicle repositioning approaches. The problem is formulated with a multi-criteria objective that minimizes the vehicles’ total travel time and maximizes their total demand-serving potential, while distributing that potential equitably among the ride-seeking passengers across the transportation network. We evaluate the proposed approach via numerical experiments using an agent-based simulation of SAMS operations in the network of Manhattan in New York City. The proposed approach is compared to a baseline simple shortest path approach for routing the repositioning vehicles. The results demonstrate that mean passenger waiting times for pick-up can be reduced, while also reducing the total vehicle miles and the empty miles travelled due to repositioning. Thus, the proposed approach can help improve the overall system performance in terms of both service quality and efficiency metrics, relative to the baseline approach.