Exploring the spatial variation of ridesourcing demand and its relationship to built environment and socioeconomic factors with the geographically weighted Poisson regression

摘要

Ridesourcing, or on-demand ridesharing, is quickly changing today's travel. Recently, research has linked socio-demographics to ridesourcing use. However, little of the research has focused on the impacts of built environment, an important factor to consider in understanding travel behavior. This study applied Geographically Weighted Poisson Regression (GWPR) and examined the spatial relationships between built environment and ridesourcing demand. We used 2016-2017 ridesourcing trip data from a transportation network company (TNC), RideAustin, in Austin, Texas. By capturing the spatial heterogeneity, the GWPRs considerably improve modeling fit compared to the global models. Modeling results present strong relationships between ridesourcing demand and built environment variables (i.e., density, land use, infrastructure, and transit accessibility). More importantly, the results demonstrate significant spatial variations of the effects of both built environment and socio-economic variables and geographic trends from urban to suburban neighborhoods. Overall, these findings suggest that built environment factors have significant impacts on ridesourcing demand, and it is important to consider the spatial context. The study provides useful insights for understanding ridesourcing use as a function of built environment and have important implications for transportation planning, demand modeling, and urban governance.