Advancements in Agent-Based Modeling for Travel Demand Forecasting (2020–2024)

This study aims to identify recent advancements in agent-based modeling (ABM) within the field of travel demand forecasting. To achieve this, peer-reviewed articles published between 2020 and 2024 were sourced from leading academic databases, including Web of Science, Scopus, TRIP (Transportation Research Integrated Database), and ScienceDirect. A total of 1,360 papers were initially retrieved. After a thorough review, 16 papers were selected for detailed analysis based on their strong alignment with the study's objectives.The selected studies were analyzed to uncover key advancements in the application of ABM to travel demand forecasting. Four main areas of progress were identified: (1) spatial-temporal demand modeling, (2) incorporation of multiple influencing factors, (3) utilization of diverse data sources, and (4) integration of multiple frameworks.To the best of our knowledge, few review studies have specifically addressed the use of ABM for travel demand forecasting. The insights from this research provide a foundation for further improvements to ABM, enabling more accurate and robust travel demand forecasting in future studies.