Research on optimization of dynamic bike-sharing repositioning and collection based on spatio-temporal demand prediction
FENG Ziyan1 , LI Xiang2 , CHANG Ximing3 , WU Jianjun3 (1. School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China; 2. School of Economics and Management, Chang'an University, Xi'an 710064, China; 3. School of System Science, Beijing Jiaotong University, Beijing 100044, China)
Abstract
As a vital component of urban transportation systems, the bike-sharing system operates on a time-based billing mode and offers point-to-point, door-to-door rental services, enabling users to conveniently pick up and drop off bicycles at their desired locations. At present, bikesharing platforms encounter operational deficiencies, including inaccurate demand predictionsuboptimal bicycle allocation, and delayed collection of faulty bikes, resulting in a significant mismatch between supply and demand. To address these challenges, this study investigates a spatiotemporal demand prediction method incorporating multi-task learning and a dynamic shared-bikes repositioning and collection approach. Firstly, a multi-gate mixture-of-experts with a bidirectional long short-term memory network is employed to jointly predict the pick-up and drop-off demands by considering the correlation between the pick-up and drop-off demands corresponding to stations. To alleviate the dependency on long-time sequences, an attention mechanism is introduced to enhance the attention given to the crucial information. Furthermore, a collaborative optimization model is proposed to address the dynamic repositioning and faulty bicycle collection in the bikesharing system, which accounts for charging decisions and mileage constraints associated with vehicles. To meet the time-sensitive requirement of large-scale dynamic repositioning management, a simulated annealing-based adaptive large neighborhood search is customized to solve the model. Finally, a comprehensive case study utilizing bike-sharing data from the New York City Citi Bike is conducted to validate the effectiveness of the proposed approach across various performance metrics: predictive accuracy, computational efficiency, and operational costs. Keywords bike-sharing; demand prediction; dynamic repositioning; multi-task learning