Agent-based models in urban transportation: review, challenges, and opportunities

ObjectiveThis paper aims to provide a comprehensive overview of the current state of agent-based models, focusing specifically on their application in urban transportation research. It identifies research gaps and challenges while outlining the key potential directions for future research.MethodologyTo conduct this study, a bibliometric analysis has been performed on 309 documents obtained from the Scopus database. The resulting clustering analysis has been further supplemented with content analysis.FindingsThe analysis reveals the existence of nine distinct clusters representing a wide range of research methods and problem interpretations in the field. In-depth examination of selected publications within each cluster has helped to identify key challenges faced by agent-based modelling approaches. These challenges include enhancing computing efficiency, developing unified calibration and validation methods, ensuring reproducibility of work, and incorporating various modules or frameworks into models to accurately replicate the complexities of the transport system and travel behaviour within specific application contexts.