A robust ensemble learning approach for human-factor based accident severity prediction: Insights from a decade of Brazilian highway data

Abstract

Traffic accidents involving heavy vehicles remain a critical safety challenge, with human factors being the primary contributors to fatality risks. While machine learning has been widely used for accident prediction, the lack of model interpretability often hinders its application in real-world policy-making. This study proposes a robust intelligent framework to classify truck driver fatalities on Brazilian federal highways by specifically isolating human-factor variables. Leveraging an extensive dataset from the Brazilian Federal Highway Police (2013–2025), we conducted a comparative analysis of three ensemble-based algorithms: Random Forest, XGBoost, and LightGBM. To ensure model stability and generalization, hyperparameter optimization was executed using RandomizedSearchCV with five-fold cross-validation. The experimental results demonstrate that while Random Forest achieved high training accuracy, LightGBM emerged as the superior model for safety-critical deployment, achieving a balanced ROC-AUC of 0.843 and a superior recall, effectively minimizing life-threatening false negatives. Furthermore, this research integrates SHAP (SHapley Additive exPlanations) to provide a knowledge-based interpretation of the model's decisions. The XAI analysis reveals that “Accident Type” and specific “Human Factor Categories” are the most significant predictors of fatality. The findings provide a transparent, data-driven decision support tool for transportation authorities to implement targeted interventions, bridging the gap between complex black-box models and actionable road safety knowledge.