Exploring Nonlinear Effects of Built Environment on Dockless Bike Sharing Usage
To investigate the dependency of dockless bike usage characteristics on the built environment,this paper used dockless bike order data and electronic fence information from Xiamen city in 2020 to analyze the nonlinear explanatory power of the built environment at both the aggregated(grid-level)and disaggregated(individual trips)levels.A machine learning model,namely,extreme gradient boosting model(XGBoost)was adopted.First,the relative importance of six dimensions of built environment variables(density,design,destination accessibility,land use diversity,public transport accessibility,and demand management)were identified on bike trip generation,attraction,and the user's departure time choice.Then,according to partial dependence plots,the impact trends and the threshold effects of built environment variables were evaluated.The results revealed that at the aggregate level,electronic fence density was the most critical factor,affecting travel generation and attraction by 26.88%and 51.88%respectively.A threshold effect was approximately 150 per·km-2.At the disaggregate level,the probability of dockless bike users borrowing bikes during the morning peak was associated with the built environment features of both the origins and destinations.Among these,the proportion of workplaces in the destination grid was the most significant factor(18.17%),followed by the proximity to Central Business District(CBD)of the origin grid(7.34%)and bus stop density in the origin grid(5.91%).
NETL
NSTL
万方数据
维普
