An Interpretable Machine Learning Model-Based Study of the Relationship between Built Environment and Residents' Daily Travel Distance

Abstract

Urban sustainability and reduced carbon emissions have become critical challenges in contemporary urban development, with residents' daily travel distances significantly impacting both the environmental and social aspects of city life. Despite extensive research on travel behavior, Crossroads Density: Number of Crossroads within 500m the complex relationship between the built environment and travel behavior remains inadequately understood due to the limitations of traditional linear analyses. To address this gap, this study introduces an analytical approach grounded in an interpretable machine learning framework to investigate the pivotal environmental factors influencing residents' daily travel distances and to understand their underlying mechanisms. Initially, a variety of machine learning regression models were constructed to evaluate their predictive efficacy regarding residents' daily travel distances. Among these, the CatBoost model demonstrated the most accurate fit, highlighting the robustness of the methodology. Subsequently, the SHAP (SHapley Additive exPlanations) methodology was employed to ascertain the significance of each feature's contribution, thereby elucidating the extent and non-linear dynamics of various built environmental indicators on travel distances. The findings indicate that the POI (Point of Interest) diversity index, the proportion of main roads, the proximity to the city center, and other factors significantly influence residents' travel distances, exhibiting observable interactive effects. Importantly, these results underscore the implications of the built environment on travel behavior, offering actionable insights for urban planners. By focusing on the built environment's impact on travel distances, this study's interpretable machine learning framework delves into the multi-factorial non-linear correlations, providing a valuable tool for enhancing urban spatial planning, reducing travel distances, lowering traffic emissions, and thereby contributing to the sustainable development of cities.