Modeling the Behavior in Choosing the Travel Mode for Long-Distance Travel Using Supervised Machine Learning Algorithms

Abstract

The long-distance travel (LDT) mode choice modeling is important for transportation planners. This study investigated alternative mode choice behavior for the LDT between the intercity buses and trains. A questionnaire survey, consisting of important mode choice attributes, was conducted on various groups of people in Bangladesh. Numerous travel mode choice contributing features (e.g., travel time, travel costs, origin-destination, comfort, safety, travel time reliability, ticket availability and schedule flexibility) were considered and the LDT mode choice models were developed using various machine learning algorithms typically applied for classification problems. With 95.31 % accuracy and 0.95 F1-score, Random Forest model was the best performing model for the dataset. According to the findings of this study, the intercity bus is preferred over the intercity train for LDT in Bangladesh.