Examining spatial heterogeneity of ridesourcing demand determinants with explainable machine learning. (arXiv:2209.07980v1 [cs.LG])

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 …

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