Factors affecting individuals’ travel mode choice in San Francisco Bay Area: a comparison between interpretable machine learning and econometric models

In this study, interpretable machine learning (ML) models and econometric models were compared to analyze individual travel mode choice behavior using data from the 2017 National Household Travel Survey in the San Francisco Bay Area. Two ML models, Random Forest (RF) and eXtreme Gradient Boosting (XGB), were implemented, along with three econometric models: Multinomial Logit, Nested Logit, and Cross-Nested Logit. Higher predictive accuracies of 0.853 for RF and 0.846 for XGB were achieved, particularly under class imbalance conditions. Model interpretability was ensured through the use of feature importance and Accumulated Local Effects plots. Promising temporal transferability was observed, as PM travel modes were accurately predicted using AM-trained models, with overall accuracies of 0.83 for RF and 0.83 for XGB, and balanced accuracies between 0.63 and 0.69. The findings indicate that the complementary strengths of ML and econometric approaches can enhance the understanding of travel behavior.