Fully Dynamic Rebalancing in Dockless Bike-Sharing Systems via Deep Reinforcement 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 an...
| Authors | Edoardo Scarpel, Alberto Pettena, Matteo Cederle et al. |
|---|---|
| Primary category | eess.SY |
| Published | 14 May 2026 |
| Last updated at source | 14 May 2026 |
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