  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.
