Revealing Spatio-Temporal Patterns and Influencing Factors of Dockless Bike Sharing Demand

Dockless bike sharing plays an important role in complementing urban transportation systems and promoting the sustainable development of cities worldwide. To improve system operational efficiency, it is critical to study the spatiotemporal patterns of dockless bike sharing demand as well as factors influencing these patterns. Based on bicycle trip data from Mobike, Point of Interest (POI) data and smart card data in Beijing, we built a spatially embedded network and implemented the Infomap algorithm, a community detection method to uncover the usage patterns. Then, the Gradient Boosting Decision Tree (GBDT) model was adopted to investigate the effect of the built environment and public transit services by controlling the temporal variables. The spatiotemporal distribution shows imbalanced characteristics. About half of the total trips occur in the morning/evening rush hours and at noon. The community detection results further reveal a polycentric pattern of trip demand distribution and 120 sub-regions with a significant difference in connection strength and scale. The result of the GBDT model indicates that factors including subway ridership, bus ridership, hour, residence density, office density have considerable impacts on trip demand, contributing about 62.6% of the total influence. Factors also represent complex nonlinear relationships with dockless bike sharing usage. The effect ranges of each factor were identified, it indicates rebalancing schemes could be changed according to spatial location. These findings may help planners and policymakers to determine the reasonable scale of bike deployment and improve the efficiency of redistribution in local regions while reducing rebalance costs.