Enhancing Travel Mode-Choice Modeling with Route-Based Attributes and Explainable Machine Learning
Modeling mode choice is essential for designing efficient and sustainable mobility systems. Revealed-preference surveys provide valuable information, but they rely on self-reported data, which can be biased and are typically unavailable for unchosen alternatives. This study proposes an integrated analytical process that combines revealed-preference survey data, route-level attributes derived from a digital trip planner, machine-learning classifiers, and explainable artificial intelligence (XAI) methods to evaluate predictive performance and behavioral interpretation jointly. Using a dataset of 1,372 trips collected in a university commuting context as an illustrative application, survey responses were enriched with mode-specific travel times and geometric characteristics of planner-recommended routes obtained from Google Maps’ application programming interface. Four tree-based classifiers were evaluated in a leak-free validation framework, and model behavior was interpreted using SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) at both the global and local levels. The results indicate that when route-level geometric attributes were combined with revealed-preference survey data, predictive accuracy remained comparable, whereas interpretability improved substantially. XAI analyses revealed that route characteristics such as straightness, sinuosity, and angular deviation emerged as significant predictors that modulated perceived travel effort, particularly for walking and public transport, despite their limited impact on aggregate performance.