Comparing machine learning and logit models in travel mode choice modeling

The increasing popularity of machine learning in transportation research raises questions regarding its advantages and disadvantages compared to conventional logit-family models used for travel behavioral analysis. We provide a comprehensive comparison between logit models and machine learning by examining the key differences in model development, evaluation, and behavioral interpretation in mode-choice modeling. We find that There appears to be a tradeoff between predictive accuracy and behavioral soundness when choosing between machine learning and logit models in mode-choice modeling.

Key words: machine learning, logit model, travel mode choice

Zhao, X., Yan, X., Yu, A., Van Hentenryck, P. (2020). Prediction and behavioral analysis of travel mode choice: A comparison of logit models and machine learning. [Download Preprint]. Travel Behavior and Society, 20, 22-35. https://doi.org/10.1016/j.tbs.2020.02.003 (Won the 2020 Outstanding Paper Award)