  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.
