Learning-based Online Optimization for Autonomous Mobility-on-Demand Fleet Control
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866916132395417600 |
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| author | Jungel, Kai Parmentier, Axel Schiffer, Maximilian Vidal, Thibaut |
| author_facet | Jungel, Kai Parmentier, Axel Schiffer, Maximilian Vidal, Thibaut |
| contents | Autonomous mobility-on-demand systems are a viable alternative to mitigate many transportation-related externalities in cities, such as rising vehicle volumes in urban areas and transportation-related pollution. However, the success of these systems heavily depends on efficient and effective fleet control strategies. In this context, we study online control algorithms for autonomous mobility-on-demand systems and develop a novel hybrid combinatorial optimization enriched machine learning pipeline which learns online dispatching and rebalancing policies from optimal full-information solutions. We test our hybrid pipeline on large-scale real-world scenarios with different vehicle fleet sizes and various request densities. We show that our approach outperforms state-of-the-art greedy, and model-predictive control approaches with respect to various KPIs, e.g., by up to 17.1% and on average by 6.3% in terms of realized profit. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2302_03963 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Learning-based Online Optimization for Autonomous Mobility-on-Demand Fleet Control Jungel, Kai Parmentier, Axel Schiffer, Maximilian Vidal, Thibaut Optimization and Control Machine Learning Autonomous mobility-on-demand systems are a viable alternative to mitigate many transportation-related externalities in cities, such as rising vehicle volumes in urban areas and transportation-related pollution. However, the success of these systems heavily depends on efficient and effective fleet control strategies. In this context, we study online control algorithms for autonomous mobility-on-demand systems and develop a novel hybrid combinatorial optimization enriched machine learning pipeline which learns online dispatching and rebalancing policies from optimal full-information solutions. We test our hybrid pipeline on large-scale real-world scenarios with different vehicle fleet sizes and various request densities. We show that our approach outperforms state-of-the-art greedy, and model-predictive control approaches with respect to various KPIs, e.g., by up to 17.1% and on average by 6.3% in terms of realized profit. |
| title | Learning-based Online Optimization for Autonomous Mobility-on-Demand Fleet Control |
| topic | Optimization and Control Machine Learning |
| url | https://arxiv.org/abs/2302.03963 |