Dynamic Traffic

Dynamic traffic research aims to understand and optimize the movement of vehicles in networks, focusing on mitigating congestion and improving efficiency. Current efforts leverage diverse approaches, including answer set programming for route optimization, reinforcement learning for adaptive traffic signal control and bandwidth allocation, and large language models for trajectory prediction, often incorporating elements like road popularity and outlier detection to enhance model accuracy and robustness. These advancements have significant implications for urban planning, autonomous driving, and network resource management, offering the potential for reduced emissions, improved travel times, and more efficient use of infrastructure.