AI-based prediction of traffic crash severity for improving road safety and transportation efficiency
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 larg...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-10970-7 |
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| author | Ayman Mohamed Mostafa Bader Aldughayfiq Mayada Tarek Alaa S. Alaerjan Hisham Allahem Murtada K. Elbashir Mohamed Ezz Eslam Hamouda |
| author_facet | Ayman Mohamed Mostafa Bader Aldughayfiq Mayada Tarek Alaa S. Alaerjan Hisham Allahem Murtada K. Elbashir Mohamed Ezz Eslam Hamouda |
| author_sort | Ayman Mohamed Mostafa |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5e436acf8d7f4d58bf90ae03e0902f29 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-5e436acf8d7f4d58bf90ae03e0902f292025-08-20T03:46:01ZengNature PortfolioScientific Reports2045-23222025-07-0115112410.1038/s41598-025-10970-7AI-based prediction of traffic crash severity for improving road safety and transportation efficiencyAyman Mohamed Mostafa0Bader Aldughayfiq1Mayada Tarek2Alaa S. Alaerjan3Hisham Allahem4Murtada K. Elbashir5Mohamed Ezz6Eslam Hamouda7Information Systems Department, College of Computer and Information Sciences, Jouf UniversityInformation Systems Department, College of Computer and Information Sciences, Jouf UniversityComputer Science Department, Faculty of Computers and Information, Mansoura UniversityComputer Science Department, College of Computer and Information Sciences, Jouf UniversityInformation Systems Department, College of Computer and Information Sciences, Jouf UniversityInformation Systems Department, College of Computer and Information Sciences, Jouf UniversityComputer Science Department, College of Computer and Information Sciences, Jouf UniversityComputer Science Department, Faculty of Computers and Information, Mansoura UniversityAbstract 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.https://doi.org/10.1038/s41598-025-10970-7Traffic crash predictionML modelsFeature engineeringClass imbalanceOversamplingFeature selection |
| spellingShingle | Ayman Mohamed Mostafa Bader Aldughayfiq Mayada Tarek Alaa S. Alaerjan Hisham Allahem Murtada K. Elbashir Mohamed Ezz Eslam Hamouda AI-based prediction of traffic crash severity for improving road safety and transportation efficiency Scientific Reports Traffic crash prediction ML models Feature engineering Class imbalance Oversampling Feature selection |
| title | AI-based prediction of traffic crash severity for improving road safety and transportation efficiency |
| title_full | AI-based prediction of traffic crash severity for improving road safety and transportation efficiency |
| title_fullStr | AI-based prediction of traffic crash severity for improving road safety and transportation efficiency |
| title_full_unstemmed | AI-based prediction of traffic crash severity for improving road safety and transportation efficiency |
| title_short | AI-based prediction of traffic crash severity for improving road safety and transportation efficiency |
| title_sort | ai based prediction of traffic crash severity for improving road safety and transportation efficiency |
| topic | Traffic crash prediction ML models Feature engineering Class imbalance Oversampling Feature selection |
| url | https://doi.org/10.1038/s41598-025-10970-7 |
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