Code implementation for the article "Spatial-Temporal Traffic Prediction: A Federated Approach with GAT and XGBoost"
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This repository contains the Python implementation for the article "Spatial-Temporal Traffic Prediction: A Federated Approach with GAT and XGBoost". The code demonstrates a novel framework integrating Graph Attention Networks, XGBoost, and SHAP for accurate and privacy-preserving traffic density prediction.
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SagharShafaati/Spatial-Temporal-Traffic-Prediction
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This repository contains the Python implementation for the article "Spatial-Temporal Traffic Prediction: A Federated Approach with GAT and XGBoost". The code demonstrates a novel framework integrating Graph Attention Networks, XGBoost, and SHAP for accurate and privacy-preserving traffic density prediction.
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