Abstract
The imbalance in Dockless Bike Sharing (DBS) systems is a major concern for planners, causing a significant drop in utilization efficiency. However, limited research quantifies DBS usage efficiency from a supply-demand perspective, also, the understanding of the nonlinear relationship between the built environment and DBS utilization efficiency from the time dimension is lacking, leading to biased assessments and the losses of flexible and effective DBS rebalancing strategies. Therefore, this study quantifies the efficiency of DBS usage from a supply-demand perspective by calculating the average usage interval of DBS facilities within urban subzones, termed duration of stopping usage (DSU), and employs emerging eXplainable Artificial Intelligence (XAI) technology to reveal the time-varying nonlinear impact of the built environment on DSU. The results show that the relative importance of transit accessibility, land use mix entropy and road network density remains stable in the time dimension. The time non-stationarity of the nonlinear relationship between these variables and DSU is primarily manifested in dynamic shifts of thresholds. Notably, the time-varying nature of the relative importance is particularly prominent for variables related to land use facilities. Moreover, the time non-stationarity of the nonlinear relationship is more complex, manifesting not only in threshold shifts but also in changes in correlation. We also propose several spatial transfer methods for DBS facilities, offering fresh insights for crafting flexible and adaptive DBS rebalancing strategies. These findings enhance the interpretability of the inconsistent impact of the built environment on DBS utilization efficiency and provide valuable knowledge for scientific management decisions regarding DBS rebalancing.
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