Abstract
Understanding the factors influencing ride‐sourcing trips is crucial for enhancing the quality of personalized mobility and optimizing the allocation of transportation system resources. However, the nonlinear effects of dockless bike‐sharing (DBS) and the built environment (BE) across different spatiotemporal contexts have not been adequately addressed in previous research. This study aims to bridge this gap by analyzing order data and BE characteristics in Tianjin, China. Utilizing the Gradient Boosting Decision Tree (GBDT) model and Accumulated Local Effects (ALE) plots, this study explores the relative importance and nonlinear thresholds of these factors on ride‐sourcing trips. The findings reveal that DBS trips during weekday AM peak hours exert significantly negative effects on ride‐sourcing, whereas the impact during weekend AM peaks and daily PM peaks is positive. Furthermore, variables such as active population density, metro accessibility, and residential, entertainment, and cultural BEs have positive nonlinear impacts on ride‐sourcing trips. These insights offer policy implications and resource allocation recommendations for both government bodies and operators.
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