Behavioral Modeling and Prediction in Urban Multimodal Transport: Current Techniques and Future Trends

In this study, modeling methods employed in multimodal transportation over the previous five years are reviewed. A thorough search of academic databases was conducted to extract articles relevant to our research objectives. The review revealed that a variety of modeling methodologies, such as logit models, machine learning algorithms, and (simulation approaches), have been used to investigate the impact of different factors on travel behavior in multimodal transportation. Among the trends in modeling techniques, one notable approach involves using generalized extreme value models, hybrid choice models, and simulations to analyze modal choice problem. For route choice problems, the predominant modeling techniques are multinomial logit models and mixed logit models.