INTEGRATED SPATIAL-TEMPORAL FEATURE ALIGNMENT WITH GRAPH CONVOLUTIONAL AND GATED RECURRENT NETWORK FOR TRAFFIC FLOW PREDICTION

The datasets support deep learning-based methodologies for traffic congestion forecasting, real-time network optimization, and spatio-temporal analysis. They can be used in: Graph Convolutional Networks (GCNs) Gated Recurrent Units (GRUs) Spatio-Temporal Feature Extraction Neural Network-based Traffic Prediction Models