| dc.description.abstract | Many people believe that paying a toll to use a managed (tolled) lane will result in a shorter travel time than using the toll-free general-purpose lanes. However, there are times users pay to travel on the toll lane but go slower than the toll-free lanes. This research examined these “uneconomical trips” on managed lanes to discover potential reasons for these trips and help understand the lane choice behavior. Some potential factors considered were toll rate, traffic flow, and past trip experience.
Random forest and logistic regression methods were implemented to examine the impact and importance of variables on the probability of a user making an uneconomical managed lane trip. This thesis showed toll rate, traffic flow, travel time variability, and trip route are key factors in predicting uneconomical managed lane trips. One challenge of this study was the fact that a small percentage of trips were uneconomical trips, which leads the model to have some bias to the major class of trips. Therefore, resampling approaches including undersampling and synthetic minority oversampling technique (SMOTE) were implemented to balance the data. This study indicated undersampling technique and random forest lead to the model with the highest accuracy.
This study can help to better understand uneconomical managed lane trips and the main factors that cause these trips. Therefore, this study provides a better understanding of travel on managed lanes and general-purpose lanes. | en |
