Discovering Extreme Commuting through the Lens of the Built Environment: An Interstation Analysis

Abstract

Although previous studies have explored disparities in commuting burdens, the association of built environment with extreme commuting still remains unclear. This study proposes a hybrid framework combining Poisson regression and gradient boosting decision trees to capture origin-destination (OD) pair attributes and nonlinear built environment associations. An empirical analysis is conducted in Shanghai, China, using large-scale rail-transit data from September 2021 and focusing on extreme commutes lasting 60 min or more during the morning and evening peak periods. Our findings reveal a pronounced symmetry in impacts of top built environment variables on extreme commuting, particularly when analyzing origin–destination pairs and contrasting morning and evening peaks. Origin-side variables during the morning peak show nonlinear associations similar to those of destination-side variables during the evening peak; notable differences exist in their effective ranges and threshold effects. Interaction results further suggest that rail-based extreme commuting is jointly associated with living costs, workplace accessibility, and local living conditions.