An Electric Fence-Based Intelligent Scheduling Method for Rebalancing Dockless Bike Sharing Systems
Lulu Jia, Dezhen Yang, Yi Ren, Qiang Feng, Bo Sun, Cheng Qian, Zhifeng Li, Chenchen Zeng
Abstract
With a new generation of bike sharing services emerging, the development of dockless bike sharing services results in considerable socioeconomic and environmental benefits but also creates new issues, such as inappropriate parking behaviors and bike imbalances. To solve the inappropriate parking problem, electric fences have been introduced to guide users to park bikes in designated zones. Considering the role of electric fences in restricting user parking behaviors, an electric fence-based intelligent scheduling method for rebalancing dockless bike sharing systems is proposed in this paper. As a dynamic method that considers the real-time usage of bike sharing systems, an electric fence adjusts its capacity based on real-time information, which guides users to return bikes to electric fences with greater urgency. Because existing approaches require prespecified models and are unable to consider all the intricacies in the dynamic optimization problem, a model-free intelligent scheduling approach based on deep Q-learning that can adapt to the changing distributions of customer arrivals, available bikes, bike locations, and user travel times is used to solve the problem. Finally, a case study involving Beihang University is employed, which shows that the method performs well in rebalancing the bike sharing system and improving the mean utilization (MU) and customer satisfaction (CS).