Predicting the Use of Managed Lanes Using Machine Learning

The use of managed lanes (MLs) is currently predicted using discrete choice models. However, recent research discovered that many travelers were not making choices in their use of MLs—they always used one set of lanes and never varied. Therefore, choice models may not be appropriate. This research examined if machine learning could be used to model ML use by travelers. Machine learning techniques were used to classify approximately 125,000 travelers who made approximately 3.5 million trips on the Katy Freeway over a 2-year period. Different machine learning models were able to classify travelers into those who always used MLs, always used toll-free general purpose lanes (GPLs), and those who used both types of lanes (choosers). Travel time savings, total number of trips made, and the start and end location of the trip were key predictors of the user class. Neural networks with LSTM could classify the trips conducted by choosers as ML trips or GPL trips based on the start and end location of the trips and the drivers’ travel history. A comparison with logit models showed that machine learning models performed better in classifying travelers and trips made by choosers. However, trends obtained from both models were same. This research has shown that a multi-level classification of travelers and machine learning techniques were able to predict real-world traveler choice on a ML facility. This may be an important first step in shifting travel models from how we think travelers are behaving to what is actually happening.