Vehicle Flow

Vehicle flow research focuses on optimizing traffic movement to minimize congestion and improve efficiency, encompassing both macroscopic (network-wide) and microscopic (individual vehicle) perspectives. Current research employs diverse methods, including graph neural networks for fusing data from various sources like cellular networks and cameras, reinforcement learning algorithms (e.g., DDDQN) for adaptive traffic signal control, and large language models for assisting in complex tasks like chip design for autonomous vehicles. These advancements aim to improve traffic management, enhance the development of autonomous driving systems, and ultimately lead to safer and more efficient transportation networks.