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

TL;DR

This paper introduces a deep reinforcement learning approach for real-time rebalancing of dockless bike-sharing systems, improving availability and reducing inequality using a graph-based simulator.

Contribution

It presents a novel DRL method that dynamically rebalances bikes in real time, overcoming limitations of traditional periodic interventions.

Findings

Significant reduction in availability failures.

Effective minimization of spatial inequality.

Improved system efficiency with minimal fleet size.

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.