Traffic Flow Prediction

Traffic flow prediction aims to forecast future traffic conditions using historical data and network characteristics, ultimately improving transportation efficiency and management. Current research heavily emphasizes the use of deep learning models, particularly graph neural networks (GNNs) and recurrent neural networks (RNNs), often incorporating attention mechanisms and advanced techniques like tensor decomposition to capture complex spatio-temporal dependencies. These models are enhanced by integrating auxiliary information (weather, events) and addressing challenges like data incompleteness and outlier detection. Improved accuracy and efficiency in these predictions have significant implications for urban planning, real-time traffic control, and the development of intelligent transportation systems.