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arXiv · 2505.07776

Robo-Taxi Fleet Coordination with Accelerated High-Capacity Ridepooling

Xinling Li·Daniele Gammelli·Alex Wallar·Jinhua Zhao·Gioele Zardini
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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.

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Xinling Li, Daniele Gammelli, Alex Wallar, Jinhua Zhao, Gioele Zardini. 2025-05-12. Robo-Taxi Fleet Coordination with Accelerated High-Capacity Ridepooling. https://arxiv.org/abs/2505.07776

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