Abstract: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.
| 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" |
| Subjects: | Systems and Control (eess.SY); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.14501 [eess.SY] |
| (or arXiv:2605.14501v1 [eess.SY] for this version) | |
| https://doi.org/10.48550/arXiv.2605.14501 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Edoardo Scarpel [view email]
[v1]
Thu, 14 May 2026 07:46:23 UTC (3,277 KB)