Examining spatial heterogeneity of ridesourcing demand determinants with explainable machine learning
This provides nuanced insights for transportation professionals managing ridesourcing services, but it is incremental as it applies existing methods to new spatial contexts.
This study tackled the problem of understanding the key determinants of ridesourcing demand by applying an explainable machine learning framework to Chicago trip data, revealing that built environment importance and nonlinear effects vary significantly across airport, downtown, and neighborhood contexts, with built environment contributing most to airport trips and demand being most responsive to changes in downtown areas.
The growing significance of ridesourcing services in recent years suggests a need to examine the key determinants of ridesourcing demand. However, little is known regarding the nonlinear effects and spatial heterogeneity of ridesourcing demand determinants. This study applies an explainable-machine-learning-based analytical framework to identify the key factors that shape ridesourcing demand and to explore their nonlinear associations across various spatial contexts (airport, downtown, and neighborhood). We use the ridesourcing-trip data in Chicago for empirical analysis. The results reveal that the importance of built environment varies across spatial contexts, and it collectively contributes the largest importance in predicting ridesourcing demand for airport trips. Additionally, the nonlinear effects of built environment on ridesourcing demand show strong spatial variations. Ridesourcing demand is usually most responsive to the built environment changes for downtown trips, followed by neighborhood trips and airport trips. These findings offer transportation professionals nuanced insights for managing ridesourcing services.