Analysis of mobility patterns for urban taxi ridership: the role of the built environment

Understanding mobility patterns of taxi ridership is important for transport planning. However, there is still room for a thorough understanding of the role of the built environment for taxi ridership across different spatial-temporal patterns. This paper proposes an analytical framework that combines non-negative CANDECOMP/PARAFAC (NCP) decomposition for pattern extraction and the light gradient boosting machine (LightGBM) for modeling the relationship between the built environment and taxi ridership. The case study was conducted in Shenzhen, China. We identified four spatial-temporal patterns by evaluating the root mean square error of the decomposition result and the representativeness of patterns. Based on the LightGBM method, we examined the nonlinear associations between taxi ridership for different patterns and the built environment. The results show that demographic characteristics are important across space. Housing-price is mainly associated with taxi ridership in western Shenzhen. Among all types of POIs, finance and entertainment are the most prominent, affecting taxi ridership in southern Shenzhen. The effects of influencing factors exhibit a high degree of localization, and mixed effects may result from localization. All variables show significant nonlinear and threshold effects on taxi ridership, and these effects could guide transport planning in different areas of the city.