Agent-based modeling in urban human mobility: A systematic review

Urban mobility is a complex system influenced by various factors such as infrastructure, technology, and human behavior. Agent-based modeling (ABM) has emerged as a valuable tool for simulating and understanding urban mobility dynamics. This paper provides a comprehensive review of ABM applications in urban human mobility, offering insights into prevailing trends in this field. The analysis of model scales highlights the predominance of area and city scales, highlighting the need for greater exploration at the intersection, metropolis, and street scales. Furthermore, the examination of technological environments shows a reliance on desktop and laptop computers, complemented by a growing adoption of specialized ABM tools such as SUMO, Anylogic, NetLogo, GAMA, and MATSim. Additionally, the study correlates ABM objectives with societal needs, revealing areas of alignment and gaps. While competitiveness and smart mobility receive considerable attention, there is a pronounced lack of focus on improving urban accessibility, sustainability, and public health. The analysis underscores the importance of addressing these gaps to ensure that ABM applications contribute effectively to addressing societal challenges. • Use of Agent-Based Models (ABM): Valuable tools for understanding urban mobility, helping unravel these complex systems. • Technological Tools: A growing use of specialized tools such as SUMO, Anylogic, Netlogo, GAMA, and MATSim. • Alignment with Societal Needs: Significant gaps in urban accessibility, sustainability, and health considerations are detected. • Accelerated Growth in Europe: Since 2015, interest in sustainable mobility has grown substantially, particularly in Europe. • Trends in Programming Tools: A shift from JAVA to Python has been observed.