A graph-attention model for accident severity prediction reports 85% Macro F1 on FARS and 84% on ARI-BUET, but the FARS label distribution is inconsistent with the dataset's own description.
Ensemble methods for accident severity prediction using regression, trees, and forests,
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STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction
A graph-attention model for accident severity prediction reports 85% Macro F1 on FARS and 84% on ARI-BUET, but the FARS label distribution is inconsistent with the dataset's own description.