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Classification of Driver Injury Severity for Accidents Involving Heavy Vehicles with Decision Tree and Random Forest

Author

Listed:
  • Aziemah Azhar

    (Vehicle Safety and Biomechanics Research Centre (VSB), Malaysian Institute of Road Safety Research (MIROS), Kajang 43000, Selangor, Malaysia)

  • Noratiqah Mohd Ariff

    (Department of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, UKM Bangi, Bandar Baru Bangi 43600, Selangor, Malaysia)

  • Mohd Aftar Abu Bakar

    (Department of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, UKM Bangi, Bandar Baru Bangi 43600, Selangor, Malaysia)

  • Azzuhana Roslan

    (Crash Data Operational & Management Unit (CRADOM), Malaysian Institute of Road Safety Research (MIROS), Kajang 43000, Selangor, Malaysia)

Abstract

Accidents involving heavy vehicles are of significant concern as it poses a higher risk of fatality to both heavy vehicle drivers and other road users. This study is carried out based on the heavy vehicle crash data of 2014, extracted from the MIROS Road Accident and Analysis and Database System (M-ROADS). The main objective of this study is to identify significant variables associated with categories of injury severity as well as classify and predict heavy vehicle drivers’ injury severity in Malaysia using the classification and regression tree (CART) and random forest (RF) methods. Both CART and RF found that types of collision, driver errors, number of vehicles involved, driver’s age, lighting condition and types of heavy vehicle are significant factors in predicting the severity of heavy vehicle drivers’ injuries. Both models are comparable, but the RF classifier achieved slightly better accuracy. This study implies that the variables associated with categories of injury severity can be referred by road safety practitioners to plan for the best measures needed in reducing road fatalities, especially among heavy vehicle drivers.

Suggested Citation

  • Aziemah Azhar & Noratiqah Mohd Ariff & Mohd Aftar Abu Bakar & Azzuhana Roslan, 2022. "Classification of Driver Injury Severity for Accidents Involving Heavy Vehicles with Decision Tree and Random Forest," Sustainability, MDPI, vol. 14(7), pages 1-19, March.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:7:p:4101-:d:783314
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    References listed on IDEAS

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    1. Takaya Saito & Marc Rehmsmeier, 2015. "The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-21, March.
    2. Lukuman Wahab & Haobin Jiang, 2019. "A comparative study on machine learning based algorithms for prediction of motorcycle crash severity," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-17, April.
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    Citations

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    Cited by:

    1. Amini, Mostafa & Bagheri, Ali & Delen, Dursun, 2022. "Discovering injury severity risk factors in automobile crashes: A hybrid explainable AI framework for decision support," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    2. Abdulrashid, Ismail & Zanjirani Farahani, Reza & Mammadov, Shamkhal & Khalafalla, Mohamed & Chiang, Wen-Chyuan, 2024. "Explainable artificial intelligence in transport Logistics: Risk analysis for road accidents," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 186(C).
    3. Maria Rodionova & Angi Skhvediani & Tatiana Kudryavtseva, 2022. "Prediction of Crash Severity as a Way of Road Safety Improvement: The Case of Saint Petersburg, Russia," Sustainability, MDPI, vol. 14(16), pages 1-20, August.
    4. Lili Zheng & Xinyu He & Tongqiang Ding & Yanlin Li & Zhengfeng Xiao, 2022. "Analysis of the Accident Propensity of Chinese Bus Drivers: The Influence of Poor Driving Records and Demographic Factors," Mathematics, MDPI, vol. 10(22), pages 1-20, November.
    5. Almudena Sanjurjo-de-No & Ana María Pérez-Zuriaga & Alfredo García, 2023. "Factors Influencing the Pedestrian Injury Severity of Micromobility Crashes," Sustainability, MDPI, vol. 15(19), pages 1-17, September.
    6. Shubing Huang & Xiaoxuan Yin & Chongming Wang & Kun Wang, 2025. "Research on Accident Severity Prediction of New Energy Vehicles Based on Cost-Sensitive Fuzzy XGBoost," Sustainability, MDPI, vol. 17(12), pages 1-15, June.

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