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Comparing fatal crash risk factors by age and crash type by using machine learning techniques

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  • Abdulaziz H Alshehri
  • Fayez Alanazi
  • Ahmed M Yosri
  • Muhammad Yasir

Abstract

This study aims to use machine learning methods to examine the causative factors of significant crashes, focusing on accident type and driver’s age. In this study, a wide-ranging data set from Jeddah city is employed to look into various factors, such as whether the driver was male or female, where the vehicle was situated, the prevailing weather conditions, and the efficiency of four machine learning algorithms, specifically XGBoost, Catboost, LightGBM and RandomForest. The results show that the XGBoost Model (accuracy of 95.4%), the CatBoost model (94% accuracy), and the LightGBM model (94.9% accuracy) were superior to the random forest model with 89.1% accuracy. It is worth noting that the LightGBM had the highest accuracy of all models. This shows various subtle changes in models, illustrating the need for more analyses while assessing vehicle accidents. Machine learning is also a transforming tool in traffic safety analysis while providing vital guidelines for developing accurate traffic safety regulations.

Suggested Citation

  • Abdulaziz H Alshehri & Fayez Alanazi & Ahmed M Yosri & Muhammad Yasir, 2024. "Comparing fatal crash risk factors by age and crash type by using machine learning techniques," PLOS ONE, Public Library of Science, vol. 19(5), pages 1-22, May.
    • Handle: RePEc:plo:pone00:0302171
    DOI: 10.1371/journal.pone.0302171
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    References listed on IDEAS

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    1. Markus D. Jakobsen & Karina Glies Vincents Seeberg & Mette Møller & Pete Kines & Patrick Jørgensen & Lasse Malchow-Møller & Alberte B. Andersen & Lars L. Andersen, 2023. "Influence of occupational risk factors for road traffic crashes among professional drivers: systematic review," Transport Reviews, Taylor & Francis Journals, vol. 43(3), pages 533-563, May.
    2. Mubarak Alrumaidhi & Mohamed M. G. Farag & Hesham A. Rakha, 2023. "Comparative Analysis of Parametric and Non-Parametric Data-Driven Models to Predict Road Crash Severity among Elderly Drivers Using Synthetic Resampling Techniques," Sustainability, MDPI, vol. 15(13), pages 1-30, June.
    3. Li, Kun & Xu, Haocheng & Liu, Xiao, 2022. "Analysis and visualization of accidents severity based on LightGBM-TPE," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
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