Robo-Taxi Fleet Coordination with Accelerated High-Capacity Ridepooling

Abstract

Rapid urbanization has led to a surge of customizable mobility demand in urban areas, which makes on-demand services increasingly popular. On-demand services are flexible while reducing the need for private cars, thus mitigating congestion and parking issues in limited urban space. While the coordination of high-capacity ridepooling on-demand service requires effective control to ensure efficiency, the emergence of the paradigm of robo-taxi opens the opportunity for centralized fleet control for an improved service quality. In this work, we propose two acceleration algorithms for the most advanced large-scale high-capacity algorithm proposed in [1]. We prove the improvement in the real-time performance of the algorithm by using real-world on-demand data from Manhattan, NYC.