Temporal Traffic

Temporal traffic modeling focuses on predicting and understanding the evolution of traffic patterns over time and space, aiming to improve transportation efficiency and urban planning. Current research heavily utilizes deep learning, employing various architectures like graph neural networks, transformers, and diffusion models to capture complex spatio-temporal dependencies within traffic data, often incorporating real-time information from diverse sources (e.g., IoT devices, satellite imagery). These advancements are crucial for optimizing traffic management systems, enhancing transportation services (ride-sharing, delivery), and developing more robust and accurate predictive models for smart cities.