Varying Built Environment Contexts and Trip Chain Decisions: A Multinomial-Choice Gradient Boosting Decision Trees Analysis

Abstract

Understanding daily trip chain decisions is important for facilitating sustainable travel behavior and reducing traffic congestion. However, the relationships between varying built environment contexts across activity locations and trip chain choices remain unclear. This study adopts a multinomial-choice gradient boosting decision trees (MC-GBDT) model to compare the relative importance of socioeconomic variables with built environment variables. Additionally, it investigates the nonlinear and threshold effects between residential areas and primary activity areas. In the 2017 Beijing household activity survey, socioeconomic variables account for only about 21% of the relative importance in predicting the trip chain choices, whereas built environment variables constitute 78%, including 46% attribute to the primary activity area and 32% to the residential area. Moreover, several built environment variables have distinctive nonlinear and threshold effects, including local access to daily facilities, road density, and transit accessibility measures. Street design features and access to public transit have greater impacts on trip chain decisions in both residential and primary activity areas. These findings echo the uncertain geographic context problem (UGCoP) and imply that traditional built environment-trip chain research might underestimate the impact of built environment contexts at primary activity locations.