Microscopic Traffic Simulation

Microscopic traffic simulation models individual vehicle movements to analyze traffic flow and optimize transportation systems. Current research emphasizes improving model accuracy by incorporating vehicle dynamics and real-world data, often using machine learning techniques like reinforcement learning and deep learning (including graph attention networks) to refine car-following and lane-changing behaviors. These advancements enable more realistic simulations for evaluating infrastructure changes, autonomous vehicle integration, and traffic control strategies, ultimately leading to improved transportation efficiency and safety.