Exploring nonlinear effects of the built environment on ridesplitting: Evidence from Chengdu

Abstract

Ridesplitting, a form of ridesourcing services that matches riders with similar routes to the same driver, is a high occupancy travel mode that can bring considerable benefits. However, the current ratio of ridesplitting in the ridesourcing services is relatively low and its influencing factors remain unrevealed. Therefore, this paper uses a machine learning method, gradient boosting decision tree (GBDT) model, to explore the nonlinear effects of built environment on the ridesplitting ratio of origin–destination pairs (census tract to census tract). The GBDT model also provides the relative importance ranking of all the built environment factors. The results indicate that distance to city center, land use diversity and road density are the key influencing factors of ridesplitting ratio. In addition, the non-linear thresholds of built environment factors are identified based on partial dependence plots, which could provide policy implications for the government and transportation network companies to promote ridesplitting.