Traffic Flow Forecasting

Traffic flow forecasting aims to predict future traffic conditions using historical data and network structure, primarily focusing on improving prediction accuracy and efficiency. Current research heavily utilizes deep learning, particularly graph neural networks (GNNs) and transformers, often incorporating adaptive graph structures and multi-channel data fusion to capture complex spatio-temporal dependencies and address challenges like long-term prediction and evolving network conditions. Accurate traffic flow forecasting is crucial for intelligent transportation systems, enabling optimized traffic management, improved infrastructure planning, and enhanced public safety.