Macroscopic Traffic

Macroscopic traffic modeling aims to understand and predict large-scale traffic patterns using aggregate measures like flow, speed, and density, rather than individual vehicle behavior. Current research emphasizes developing data-driven models, often employing neural networks (including transformers and physics-informed neural operators) to learn complex relationships from diverse data sources like remote sensing imagery and traffic sensors. These advancements improve traffic state estimation, particularly in areas with limited sensor coverage, and enable more accurate prediction of congestion and traffic wave propagation, informing better traffic management and control strategies. The ultimate goal is to enhance the efficiency and safety of transportation systems.