Fully Dynamic Rebalancing in Dockless Bike-Sharing Systems via Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2605.14501v1
• Date: Thu, 14 May 2026 07:46:23 GMT
• Title: Fully Dynamic Rebalancing in Dockless Bike-Sharing Systems via Deep Reinforcement Learning
• Abstract summary: This paper proposes a fully dynamic Deep Reinforcement Learning (DRL) method for rebalancing dockless bike-mobility systems.<n>We model the service through a graph-based simulator and cast rebalancing as a Markov decision process.
• Score: 16.258886980394397
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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