Abstract Manager

Agent-based modeling (ABM) has become a cornerstone in transportation and traffic engineering, offering a versatile framework for simulating complex systems. By representing individual entities—such as vehicles, pedestrians, and cyclists—as autonomous agents, ABMs provide detailed insights into traffic flow, travel behavior, and policy impacts. This review paper critically examines the state of the art in ABM applications for transportation, focusing on best practices and innovative approaches to address contemporary challenges. 

A systematic review of existing studies highlights the flexibility of ABM in analyzing diverse scenarios, from urban mobility and logistics to driver behavior and infrastructure optimization. For instance, ABMs have been successfully employed to evaluate autonomous vehicle (AV) systems, revealing their potential to enhance urban mobility networks and reduce environmental impacts. However, gaps remain in integrating AVs for combined passenger and freight purposes. Similarly, the application of ABMs to optimize traffic systems, such as traffic light scheduling and coordinated semaphore systems, has demonstrated significant performance improvements, underscoring their utility for real-time traffic management. 

Moreover, the adaptability of ABMs to incorporate human decision-making and heterogeneous agent behaviors positions them as a powerful tool for understanding future mobility patterns. Key advancements include the integration of genetic algorithms, 3D modeling environments, and multi-agent simulation platforms like MATSim. These technologies have enhanced the realism and scalability of ABM-based simulations, allowing for the exploration of complex interactions in metropolitan areas, such as those arising from population growth, suburbanization, and shifts in travel behavior. 

Despite these advancements, challenges persist, including high computational demands, data scarcity, and the need for robust calibration and validation frameworks. This paper identifies opportunities to leverage recent innovations, such as artificial intelligence and digital connectivity, to overcome these hurdles and expand the applicability of ABM in transportation research. 

By presenting best practices and highlighting emerging trends, this paper aims to bridge the gap between theoretical advancements and practical applications. It serves as a comprehensive guide for researchers and practitioners seeking to utilize ABM in developing sustainable, efficient, and adaptive transportation systems.