The dock-less bike-sharing system (DBSS) is a novel mode that allows users to rent/return available sharing bikes without fixed docking station requirements. To increase the usage of idle bikes and fulfill the spatial-temporal varying user demand, the operator needs to reposition available bikes periodically. This paper proposed a multi-agent reinforcement learning framework (MARL) for generating dynamic bike repositioning plans for DBSS to minimize user dissatisfaction. The framework introduced the double deep q network (DDQN) algorithm with the shadow environment trick to tackle the non-stationary learning problem and decomposed training process into sequential independent single-agent training. The bike-sharing data in Yangpu and Hongkou Districts, Shanghai, China, was implemented for model validation. The validation results demonstrated that the proposed approach can achieve robust performance in various condition with different truck and bike fleet size and truck capacity. It also outperformed some traditional stage-optimization model in reducing user dissatisfaction.
Dynamic Repositioning in Dock-less Bike-sharing System: A Multi-agent Reinforcement Learning Approach
2022-10-08
611788 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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