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.
@article{arxiv.2605.14501,
title = {Fully Dynamic Rebalancing in Dockless Bike-Sharing Systems via Deep Reinforcement Learning},
author = {Edoardo Scarpel and Alberto Pettena and Matteo Cederle and Federico Chiariotti and Marco Fabris and Gian Antonio Susto},
journal= {arXiv preprint arXiv:2605.14501},
year = {2026}
}
Comments
6 pages, 5 figures, 1 table, accepted at the 23rd IFAC World Congress, Busan, South Korea, Aug. 23-26, 2026. Open invited track 9-131: "Control and Optimization for Smart Cities"