Fully Dynamic Rebalancing in Dockless Bike-Sharing Systems via Deep Reinforcement Learning
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arXiv
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| Format: | Preprint |
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2026
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| author | Scarpel, Edoardo Pettena, Alberto Cederle, Matteo Chiariotti, Federico Fabris, Marco Susto, Gian Antonio |
| author_facet | Scarpel, Edoardo Pettena, Alberto Cederle, Matteo Chiariotti, Federico Fabris, Marco Susto, Gian Antonio |
| contents | This paper proposes a fully dynamic Deep Reinforcement Learning (DRL) method for rebalancing dockless bike-sharing systems, overcoming the limitations of periodic, system-wide interventions. We model the service through a graph-based simulator and cast rebalancing as a Markov decision process. A DRL agent routes a single truck in real time, executing localized pick-up, drop-off, and charging actions guided by spatiotemporal criticality scores. Experiments on real-world data show significant reductions in availability failures with a minimal fleet size, while limiting spatial inequality and mobility deserts. Our approach demonstrates the value of learning-based rebalancing for efficient and reliable shared micromobility. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_14501 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Fully Dynamic Rebalancing in Dockless Bike-Sharing Systems via Deep Reinforcement Learning Scarpel, Edoardo Pettena, Alberto Cederle, Matteo Chiariotti, Federico Fabris, Marco Susto, Gian Antonio Systems and Control Artificial Intelligence Machine Learning This paper proposes a fully dynamic Deep Reinforcement Learning (DRL) method for rebalancing dockless bike-sharing systems, overcoming the limitations of periodic, system-wide interventions. We model the service through a graph-based simulator and cast rebalancing as a Markov decision process. A DRL agent routes a single truck in real time, executing localized pick-up, drop-off, and charging actions guided by spatiotemporal criticality scores. Experiments on real-world data show significant reductions in availability failures with a minimal fleet size, while limiting spatial inequality and mobility deserts. Our approach demonstrates the value of learning-based rebalancing for efficient and reliable shared micromobility. |
| title | Fully Dynamic Rebalancing in Dockless Bike-Sharing Systems via Deep Reinforcement Learning |
| topic | Systems and Control Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2605.14501 |