MGraph: Adaptive Multi-Graph Neural Network for Dynamic Spatial-Temporal Traffic Flow Forecasting

This dataset supports the paper \u201cMGraph: Adaptive Multi-Graph Neural Network for Dynamic Spatial-Temporal Traffic Flow Forecasting,\u201d published in IEEE Transactions on Intelligent Transportation Systems. It contains experimental results for the MGraph model, which predicts traffic flow using adaptive adjacency matrices and a weekly regularity scheme. The dataset includes txt files with predictions, ground truth, and evaluation metrics (MAE, RMSE, MAPE) for five traffic datasets (PeMS03, PeMS04, PeMS07, PeMS08, METR-LA) across four prediction horizons (15, 30, 45, 60 minutes). Organized by model, dataset, and random seed (1\u201310), the files enable reproducibility of MGraph\u2019s performance and benchmarking against baselines like GWNet and STID. Researchers can use this dataset to validate results, compare new models, or analyze traffic patterns. Code is available at https:\/\/github.com\/JABUBROWN\/MGraph.