用深度强化学习动态调度单车，减少空车和无车区。
Fully Dynamic Rebalancing in Dockless Bike-Sharing Systems via Deep Reinforcement Learning
- 用强化学习实时指挥一辆调度车，按时空热点自动取还车。
- 真实数据测试显示故障率大幅下降，车队规模更小。
- 适合研究共享出行调度或城市交通优化的读者。
本文提出一种全动态深度强化学习方法，用于无桩共享单车系统的再平衡，克服了传统周期性、全局干预的局限。我们通过基于图的仿真器建模服务系统，并将再平衡问题建模为马尔可夫决策过程。一个DRL智能体实时调度单辆卡车，根据时空关键性评分执行局部取车、放车和充电操作。在真实世界数据上的实验表明，该方法显著降低了车辆可用性故障，仅需最小车队规模，同时缓解了空间不平等和出行盲区问题。结果证明了基于学习的再平衡策略在提升共享微出行效率与可靠性方面的价值。
原文摘要 · Abstract (English)
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.
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