Unravel the spatio-temporal patterns and their nonlinear relationship with correlates of dockless shared bikes near metro stations

Abstract

ABSTRACTThe dockless bike-sharing system has rapidly expanded worldwide and has been widely used as an intermodal transport to connect with public transportation. However, higher flexibility may cause an imbalance between supply and demand during daily operation, especially around the metro stations. A stable and efficient rebalancing model requires spatio-temporal usage patterns as fundamental inputs. Therefore, understanding the spatio-temporal patterns and correlates is important for optimizing and rescheduling bike-sharing systems. This study proposed a dynamic time warping distance-based two-dimensional clustering method to quantify spatio-temporal patterns of dockless shared bikes in Wuhan and further applied the multiclass explainable boosting machine to explore the main related factors of these patterns. The results found six patterns on weekdays and four patterns on weekends. Three patterns show the imbalance of arrival and departure flow in the morning and evening peak hours, while these phenomena become less intensive on weekends. Road density, living service facility density and residential density are the top influencing factors on both weekdays and weekends, which means that the comprehensive impact of built-up environment attraction, facility suitability and riding demand leads to the different usage patterns. The nonlinear influence universally exists, and the probability of a certain pattern varies in different value ranges of variables. When the densities of living facilities and roads are moderate and the relationship between job and housing is relatively balanced, it can effectively promote the balanced usage of dockless shared bikes while maintaining high riding flow. The spatio-temporal patterns can identify the associated problems such as imbalance or lack of users, which could be mitigated by corresponding solutions. The relative importance and nonlinear effects help planners prioritize strategies and identify effective ranges on different patterns to promote the usage and efficiency of the bike-sharing system.

Keywords

• Dockless shared bikes
• spatio-temporal patterns
• machine learning
• transit oriented development