Download Report IEEE DataPort : Modulated Graph Neural Network for Dynamic Spatiotemporal Traffic Forecasting - 2025
TXT by Jabulani Mpofu
Information
Format: TXT Publisher: IEEE DataPort Publication Date of the Electronic Edition: 12/11/2025
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ISBN: 10.21227/matk-gf81
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Description
This dataset supports the research presented in the article “Modulated Graph Neural Network for Dynamic Spatiotemporal Traffic Forecasting,” submitted to submitted to Knowledge-Based 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 four traffic datasets (PeMS03, PeMS04, PeMS07, PeMS08) across four prediction horizons (15, 30, 45, 60 minutes). Organized by model, dataset, and random seed (1–10), the files enable reproducibility of MGraph’s 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.
$15
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Delivery time: Instant