Usage Patterns Identification and Flow Prediction of Bike-sharing System based on Multiscale Spatiotemporal Clustering

At present, China government and bike-sharing companies mostly use electronic fence parking stations to manage the shared bicycles normatively. Electric fence parking stations for free-floating bike-sharing are predetermined 'virtual fences' to guide users to park bikes in designated zones and regulate inappropriate parking behaviors. However, due to the randomness and uncertainty of the inflow and outflow of bicycles at a single parking station, the scheduling of bicycles based on an independent parking station is hard to realize. Therefore, it is necessary to group fence stations into clusters and implement regional management. In this paper, we proposed a network clustering algorithm based on spatiotemporal constraints, which comprehensively considered spatial factors (location and geographical environment of the parking stations) and temporal factors (historical bike-sharing system orders) as the clustering partition basis, and this algorithm can realize the multi-scale groups division of parking stations only by setting a distance threshold. We chose Xiamen Island as the research region. Using the distance thresholds of 3000 m and 700 m respectively, we carried out clustering experiments on the electronic fence parking stations in the whole Xiamen Island and its Wushipu block. The results showed that this algorithm can not only gather the parking stations with similar temporal and spatial characteristics into the same group, but also make the shared bike flow mainly concentrated in the streets within each group, which is convenient for regional management. Then, we mined the characteristics of shared bikes among the partitioned groups, which can effectively identify and locate hot areas for shared bikes. The results showed that subway stations, office buildings, parks, hospitals, shopping malls, and residential areas had a greater impact on the usage pattern of shared bikes. In particular, it is necessary to focus on the accumulation of shared bikes near office buildings, shopping malls, hospitals, and subway stations, and the shortage of bicycles near the residential areas, parks, and factories during the morning rush hours. Finally, we used the Long Short Time Memory network (LSTM) to predict the orders of shared bikes. The results showed that 84% of the groups had a prediction accuracy of more than 85%, and the average of the overall prediction accuracy was 91.301%, which can meet the needs of bike-sharing system scheduling. Our research provides scientific suggestions for relevant departments to arrange electronic fence parking stations, and the LSTM model has high accuracy in predicting bicycle flow, which is effective in reducing the scheduling cost of bike-sharing system and improve the management efficiency. © 2022, Science Press. All right reserved.