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<p>Crash injury severity prediction in heterogeneous traffic remains challenging due to complex behavioural–vehicle interactions and limited interpretability of data-driven models. This study proposes a mathematically formulated Random Forest (RF) framework for crash severity classification along NH-44, India, integrating rigorous preprocessing, matrix-structured model representation, and perturbation-based sensitivity evaluation. A key contribution of this work is its move beyond isolated predictors to model interpretable driver–vehicle interactions, validated through Gini-importance and perturbation sensitivity. The model demonstrates high multi-class predictive performance, achieving 88.7% test-set classification accuracy, an OvR mean ROC-AUC of 0.94, and a macro-averaged F1 score of 0.91. Comparative testing confirms the proposed RF framework provides superior, stable crash-severity discrimination, adding clear value for heterogeneous traffic. This approach advances beyond black-box models by linking predictions to interpretable safety factors, enabling targeted interventions for high-risk conditions. It provides a scalable, transferable tool for evidence-based safety decisions in heterogeneous traffic.</p>

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A matrix-structured data-driven random forest framework for predicting road accident severity under heterogeneous traffic conditions

  • 期刊论文

  • 发表日期：

    2026年5月4日

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  • Tazim Ameen,
  • Abdullah Ahmad

摘要

Crash injury severity prediction in heterogeneous traffic remains challenging due to complex behavioural–vehicle interactions and limited interpretability of data-driven models. This study proposes a mathematically formulated Random Forest (RF) framework for crash severity classification along NH-44, India, integrating rigorous preprocessing, matrix-structured model representation, and perturbation-based sensitivity evaluation. A key contribution of this work is its move beyond isolated predictors to model interpretable driver–vehicle interactions, validated through Gini-importance and perturbation sensitivity. The model demonstrates high multi-class predictive performance, achieving 88.7% test-set classification accuracy, an OvR mean ROC-AUC of 0.94, and a macro-averaged F1 score of 0.91. Comparative testing confirms the proposed RF framework provides superior, stable crash-severity discrimination, adding clear value for heterogeneous traffic. This approach advances beyond black-box models by linking predictions to interpretable safety factors, enabling targeted interventions for high-risk conditions. It provides a scalable, transferable tool for evidence-based safety decisions in heterogeneous traffic.