AI-based prediction of traffic crash severity for improving road safety and transportation efficiency.

Journal: Scientific reports
Published Date: Jul 28, 2025

Abstract

Ensuring safe transportation requires a comprehensive understanding of driving behaviors and road safety to mitigate traffic crashes, reduce risks and enhance mobility. This study introduces an AI-driven machine learning (ML) framework for traffic crash severity prediction, utilizing a large-scale dataset of over 2.26 million records. By integrating human, crash-specific, and vehicle-related factors, the model improves predictive accuracy and reliability. The methodology incorporates feature engineering, clustering techniques such as K-Means and HDBSCAN, with oversampling methods such as RandomOverSampler, SMOTE, Borderline-SMOTE, and ADASYN to address class imbalance, along with Correlation-Based Feature Selection (CFS) and Recursive Feature Elimination (RFE) for optimal feature selection. Among the evaluated classifiers, the Extra Trees (ET Classifier) ensemble model demonstrated superior performance, achieving 96.19% accuracy and an F1-score (macro) of 95.28%, ensuring a well-balanced prediction system. The proposed framework provides a scalable, AI-powered solution for traffic safety, offering actionable insights for intelligent transportation systems (ITS) and accident prevention strategies. By leveraging advanced ML and feature selection techniques, this approach enhances traffic risk assessment and enables data-driven decision-making.

Authors

  • Ayman Mohamed Mostafa
    Information Systems Department, College of Computer andInformation Sciences, Jouf University, Sakaka, Saudi Arabia.
  • Bader Aldughayfiq
    Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakakah 42421, Saudi Arabia.
  • Mayada Tarek
    Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt.
  • Alaa S Alaerjan
    Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka, 72388, Saudi Arabia.
  • Hisham Allahem
    Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakakah 42421, Saudi Arabia.
  • Murtada K Elbashir
    College of Computer and Information Sciences, Jouf University, Sakaka, 72441, Saudi Arabia.
  • Mohamed Ezz
    Computer ScienceDepartment, College of Computer and Information Sciences, Jouf University, Sakaka,Saudi Arabia.
  • Eslam Hamouda
    Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt.

Keywords

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