Examining the nonlinear impacts of built environment on ridesourcing usage: Focus on the critical urban sub-regions

Abstract

Ridesourcing or on-demand ridesharing, offers a sustainable mobility option that connects drivers with passengers via mobile application directly, which helps reduce unnecessary vehicle cruising and energy consumption. It plays a crucial role in urban mobility within the built environment. However, the interdependency between ridesourcing usage and built environment has not been addressed adequately, particularly in the critical regions that have significant influence on ridesourcing usage in an urban context. Based on percolation theory, this study suggests a new concept, namely ridesourcing usage islands, defined as geographical areas of interest with a high or low concentration of ridesourcing usage. Within these noteworthy areas, a machine learning method, gradient boosting decision trees (GBDT), is further innovatively adopted to investigate the refined and discontinuous non-linear impacts of built environment on ridesourcing usage. The results reveal a hierarchical structure of ridesourcing usage islands. Regional imbalances of travel supply and demand at usage island level are sporadically identified across several regions. Besides, the formation of usage islands is highly influenced by the surrounding built environment. Most importantly, employment density and residential density have joint contribution of almost 20% for ridesourcing pick up demand and drop off demand respectively, reflecting the role of ridesourcing in commuting. Regardless of island's type, built environment features show obvious threshold effects on ridesourcing usage, and their specific effective ranges are different from each other. Findings in this paper are expected to help better understand ridesourcing use as a function of urban built environment, and provide valuable inputs for ridesourcing management and sustainable urban development.