Multi Scale Traffic

Multi-scale traffic analysis aims to understand and predict traffic patterns across various spatial and temporal scales, from individual intersections to entire city networks. Current research focuses on developing sophisticated AI models, including deep learning architectures like convolutional neural networks (CNNs) and recurrent neural networks (RNNs), often integrated with graph neural networks (GNNs) to handle complex road network structures, for improved traffic prediction and anomaly detection. These advancements are crucial for optimizing traffic management, enhancing transportation infrastructure planning, and improving the efficiency and safety of transportation systems. The development of large, realistic traffic datasets is also a key area of focus, enabling the training and validation of these advanced models.