Robo-Taxi Fleet Coordination with Accelerated High-Capacity Ridepooling
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866918132800552960 |
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| author | Li, Xinling Gammelli, Daniele Wallar, Alex Zhao, Jinhua Zardini, Gioele |
| author_facet | Li, Xinling Gammelli, Daniele Wallar, Alex Zhao, Jinhua Zardini, Gioele |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_07776 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Robo-Taxi Fleet Coordination with Accelerated High-Capacity Ridepooling Li, Xinling Gammelli, Daniele Wallar, Alex Zhao, Jinhua Zardini, Gioele Optimization and Control 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. |
| title | Robo-Taxi Fleet Coordination with Accelerated High-Capacity Ridepooling |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2505.07776 |