Systematic Review of Transportation Choice Modeling

This research presents an overview of transportation mode choice, emphasizing key influencing factors and a range of methodological approaches from traditional Random Utility Theory (RUT) models to modern Machine Learning (ML) techniques. A comprehensive review covered 875 papers, which were screened for relevance. The search was conducted on ScienceDirect and Google Scholar between October and November 2024 using the keywords transport and choice model. Search results were reviewed until several consecutive entries no longer contained content relevant to the topic. After the screening and exclusion process, 106 papers remained for analysis. The review reveals that the Multinomial Logit (MNL) model remains the most widely used approach for modeling transportation mode choice, despite a growing interest in ML methods. Cars and buses dominate in passenger transport studies, while trucks, trains, and ships are most common in freight research. Data is typically collected through surveys (for passenger transport) and interviews (for freight), though some studies use secondary sources. Geographically, Asia and Europe are most represented, with regions like South America underrepresented. Travel time and cost are key variables, with increasing attention to the built environment in passenger studies and service reliability in freight studies. Overall, most studies aim to address real-world transport challenges. The review highlights the persistent gap between theoretical advancements and real-world applicability. To support this analysis, it examines the specific research objectives and findings of each study.