Interpretable Machine Learning Models for Modal Split Prediction in Transportation Systems

TL;DR

This paper develops interpretable logistic regression models with regularization for hourly modal split prediction in transportation, highlighting important segments and improving accuracy over traditional methods.

Contribution

It introduces a regularized logistic regression approach that enhances interpretability and prediction accuracy for modal split forecasting using high-dimensional travel data.

Findings

Model identifies key network segments influencing modal splits.

Regularization improves prediction accuracy and interpretability.

Method outperforms pre-specified variable selection techniques.

Abstract

Modal split prediction in transportation networks has the potential to support network operators in managing traffic congestion and improving transit service reliability. We focus on the problem of hourly prediction of the fraction of travelers choosing one mode of transportation over another using high-dimensional travel time data. We use logistic regression as base model and employ various regularization techniques for variable selection to prevent overfitting and resolve multicollinearity issues. Importantly, we interpret the prediction accuracy results with respect to the inherent variability of modal splits and travelers' aggregate responsiveness to changes in travel time. By visualizing model parameters, we conclude that the subset of segments found important for predictive accuracy changes from hour-to-hour and include segments that are topologically central and/or highly congested. We apply our approach to the San Francisco Bay Area freeway and rapid transit network and demonstrate superior prediction accuracy and interpretability of our method compared to pre-specified variable selection methods.