Real Time Traffic

Real-time traffic analysis aims to understand and predict traffic flow dynamics for improved transportation management and safety. Current research heavily utilizes deep learning, employing architectures like graph neural networks (GNNs), convolutional neural networks (CNNs, including 3DResNet and Sparse-UNet), and generative adversarial networks (GANs) to process large-scale spatiotemporal traffic data, often integrating large language models (LLMs) for enhanced interpretability and control. These advancements are crucial for optimizing traffic signal control, autonomous vehicle navigation, and incident detection, ultimately leading to reduced congestion, improved efficiency, and enhanced safety in transportation systems.