Traffic Dynamic

Traffic dynamics research focuses on understanding and predicting the complex movement of vehicles, aiming to improve traffic flow efficiency and safety. Current research emphasizes developing sophisticated models, including neural differential equations, graph neural networks, and reinforcement learning algorithms, to capture the spatiotemporal complexities of traffic flow in diverse environments, from single intersections to large-scale networks. These advancements are crucial for optimizing traffic signal control, autonomous vehicle navigation, and traffic forecasting, ultimately leading to improved urban planning and transportation management. The field is also seeing increased use of hybrid models combining data-driven approaches with physics-based understanding to enhance both accuracy and interpretability.