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A methodological framework for road accident severity prediction for indian highways using machine learning models.

Humera Khanum1,2, Anshul Garg2, Mir Iqbal Faheem3

  • 1Civil Engineering, Symbiosis Institute of Technology, Symbiosis Knowledge Village, Near Lupin Research Park, Gram Lavale, Mulshi, Pune, 412115, Maharashtra, India.

Methodsx
|December 11, 2025
PubMed
Summary

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This study uses machine learning to predict road accident severity in India, identifying key factors like vehicle type and road conditions. The SHAP-enhanced model offers interpretable insights for improved road safety measures.

Area of Science:

  • Road safety
  • Machine learning applications
  • Traffic accident analysis

Background:

  • Road traffic accidents pose a significant global threat, with India experiencing over 150,000 annual fatalities.
  • Existing models struggle to accurately represent the complex interactions of accident risk factors.
  • There is a need for advanced analytical frameworks to understand and mitigate accident severity.

Purpose of the Study:

  • To develop and evaluate machine learning models for predicting road accident severity in India.
  • To enhance model interpretability using SHAP values for identifying critical influencing factors.
  • To provide a data-driven framework for improving highway safety and informing policy.

Main Methods:

  • Implementation of Random Forest and Gradient Boosting algorithms for severity prediction.
Keywords:
Gradient boosting modelIndian highwaysMachine learning modelsRandom forest modelRoad accident severity prediction

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  • Application of SHAP (SHapley Additive exPlanations) values to determine feature importance.
  • Evaluation of model performance using standard metrics like accuracy, precision, recall, and F1-score.

    • Main Results:

    • Vehicle type, accident location, and road conditions were identified as significant predictors of accident severity.
    • The SHAP-enhanced models provided clear insights into the contribution of each factor.
    • Model performance metrics demonstrated the effectiveness of the proposed framework.

    Conclusions:

    • The SHAP-enhanced machine learning approach offers a robust and interpretable method for predicting road accident severity.
    • Findings provide actionable insights for developing targeted road safety interventions in India.
    • This framework supports proactive safety measures and infrastructure improvements on highways.