Traffic Management

Traffic management research aims to optimize traffic flow and safety, focusing on efficient control strategies and accurate prediction models. Current efforts concentrate on leveraging machine learning, particularly reinforcement learning and large language models, alongside advanced architectures like graph neural networks and computer vision techniques (e.g., YOLOv8, Faster R-CNN) to analyze diverse data sources (images, sensor data, V2X communication). These advancements hold significant potential for improving urban mobility, autonomous driving systems, and overall transportation efficiency by enabling real-time adaptive control and more accurate predictions of traffic patterns.