Fully Dynamic Rebalancing in Dockless Bike-Sharing Systems via Deep Reinforcement Learning
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 25 references that are in the map. First posted on arXiv 14 May 2026.
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References · 8
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
- 2017Graph Attention Networkscited by 8,854
- 2015Fast R-CNNcited by 2,930
- 2015Deep Reinforcement Learning with Double Q-learningcited by 1,704
- 2018A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systemscited by 13
- 2024Efficient Sensors Selection for Traffic Flow Monitoring: An Overview of Model-Based Techniques leveraging Network Observabilitycited by 3
- 2022Visual Sensor Network Stimulation Model Identification via Gaussian Mixture Model and Deep Embedded Featurescited by 2
- 2024A Fairness-Oriented Reinforcement Learning Approach for the Operation and Control of Shared Micromobility Servicescited by 2
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