Real-time reposition management of bike-sharing systems: a synchronous predict-then-optimize approach

Zifan Kang, Ximing Chang, Huijun Sun and Xin Guo

Transportation Research Part A: Policy and Practice, 2025, vol. 201, issue C

Abstract: As a convenient and low-carbon transport service to address the “last mile” problem, bike-sharing systems (BSSs) have been rapidly developed worldwide. However, the salient spatiotemporal imbalance between demand and supply has led to the bike-sharing repositioning problem (BSRP), aiming to reposition bikes from surplus stations to insufficient stations efficiently in BSS. This paper proposes a synchronous prediction then instantaneous optimization (SPtIO) approach, which consists of a multi-task multi-gate mixture of topology adaptive graph convolutional networks (3M−TAGCN) station relocation demand prediction model and a transformer policy-based reinforcement learning (TPRL) bike-sharing repositioning model. The 3M−TAGCN model makes synchronous and dynamic predictions for the real-time relocation demands of all stations by learning features and relationships from historical inflow and outflow spatiotemporal data. Leveraging the advantage of “offline training + online optimizing”, the TPRL model instantaneously figures out the dynamic BSRP based on predicted relocation demands. The policy of the TPRL model consists of a transformer network and a mask method, and the training process incorporates the policy gradient algorithm. Experiments on the New York Citi Bike dataset demonstrate that the 3M−TAGCN prediction model outperforms other baseline models in various scenarios. The TPRL bike-sharing repositioning model effectively determines near-optimal repositioning schemes. Evident results have shown significant improvements in the proposed SPtIO approach over the service quality and repositioning efficiency of BSSs.

Keywords: Bike-sharing system; Reposition management; Route optimization; Multi-task learning; Reinforcement learning (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.tra.2025.104678

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