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

摘要: 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 …