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<p>Highway tunnels are locations where traffic accidents frequently occur; thus, accurately predicting and analyzing these incidents is crucial for alleviating traffic congestion and enhancing operational management. This article utilizes data from the Norwegian National Road Public Database spanning 2010 to 2020 to conduct a multifaceted feature analysis of traffic accidents in 274 tunnels, focusing on severity, spatiotemporal distribution, and vehicle type. Initially, ten highly relevant influencing factors were identified through a chi-square test, and the association rule method was employed to uncover intrinsic correlations among the variables. Subsequently, the SHAP (Shapley Additive exPlanations) method facilitated an interpretability analysis, elucidating the contributions and nonlinear impacts of factors such as weather, road conditions, and vehicle types on accident severity, while also revealing the interactive effects among key variables. For accident level prediction, a random forest model was developed, with a confusion matrix and ROC (Receiver Operating Characteristic) curve serving as evaluation metrics for comparison with Bayesian networks and XGBoost (eXtreme Gradient Boosting). The findings indicate that the random forest model outperforms the others, achieving an accuracy of 86% and an AUC (Area Under the Curve) value of 0.89. This research holds significant practical value for the refinement and intelligent management of tunnel traffic safety.</p>

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Prediction of Traffic Accident Grade in Highway Tunnels Based on Random Forest

  • 期刊论文

  • 发表日期：

    2026年4月14日

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  • Xuejing Du,
  • Guorui Li,
  • Zhanyu Wang,
  • Aihui Wang

摘要

Highway tunnels are locations where traffic accidents frequently occur; thus, accurately predicting and analyzing these incidents is crucial for alleviating traffic congestion and enhancing operational management. This article utilizes data from the Norwegian National Road Public Database spanning 2010 to 2020 to conduct a multifaceted feature analysis of traffic accidents in 274 tunnels, focusing on severity, spatiotemporal distribution, and vehicle type. Initially, ten highly relevant influencing factors were identified through a chi-square test, and the association rule method was employed to uncover intrinsic correlations among the variables. Subsequently, the SHAP (Shapley Additive exPlanations) method facilitated an interpretability analysis, elucidating the contributions and nonlinear impacts of factors such as weather, road conditions, and vehicle types on accident severity, while also revealing the interactive effects among key variables. For accident level prediction, a random forest model was developed, with a confusion matrix and ROC (Receiver Operating Characteristic) curve serving as evaluation metrics for comparison with Bayesian networks and XGBoost (eXtreme Gradient Boosting). The findings indicate that the random forest model outperforms the others, achieving an accuracy of 86% and an AUC (Area Under the Curve) value of 0.89. This research holds significant practical value for the refinement and intelligent management of tunnel traffic safety.