Microscopic Traffic

Microscopic traffic modeling focuses on simulating individual vehicle interactions to understand and predict overall traffic flow, aiming to improve traffic management and autonomous driving systems. Current research emphasizes developing more realistic car-following models, often incorporating machine learning techniques like reinforcement learning and graph neural networks, to better capture driver behavior and predict travel times under various conditions. These advancements are crucial for optimizing traffic signal control, enhancing autonomous vehicle decision-making, and creating more efficient and safer transportation systems.