Traffic State Estimation

Traffic state estimation (TSE) aims to accurately determine the current conditions of traffic flow, such as speed and density, often using limited or incomplete data from various sources like sensors and connected vehicles. Recent research heavily utilizes deep learning models, particularly physics-informed deep learning (PIDL) and other neural network architectures like convolutional and recurrent networks, often incorporating spatial and temporal attention mechanisms to improve accuracy and efficiency, especially in sparse data scenarios. These advancements are crucial for improving traffic management, enabling real-time predictions of congestion and incidents, and optimizing traffic control strategies, ultimately leading to safer and more efficient transportation systems.