A Dual-Track Feature Fusion and Interpretable Prediction Framework for Transportation Accident Severity Under Small-Sample and Class-Skew Constraints

Accurately predicting transportation accident severity is critical for targeted risk governance, yet research based on accident investigation reports is often hampered by small sample sizes and skewed class distributions. This study develops a dual-track feature fusion and interpretable prediction framework to overcome these constraints. More than 1000 candidate documents were screened, yielding a reconstructed analytical sample of 157 eligible accident investigation reports for a three-class accident severity classification task. The methodology integrates HFACS-Lite vertical hierarchy and DEMATEL-Lite horizontal coupling to construct high-order fused features, employing the TabPFN foundation model as the backbone learner alongside SMOTENC and post hoc dual-threshold adjustments. Empirical results show that the final SMOTENC-enhanced dual-track TabPFN achieved an accuracy of 0.783 and a Macro-F1 of 0.717, while post hoc dual-threshold adjustment increased major-and-above recall to 0.6429. SHAP attribution indicates that high-consequence accidents are associated with joint patterns of micro-level operations, operating scenarios, and safety governance. The proposed framework supports association-based severity classification and risk screening under constrained data conditions.