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Predicting Crash Injury Severity with Machine Learning Algorithm Synergized with Clustering Technique: A Promising Protocol

Author

Listed:
  • Khaled Assi

    (Civil and Environmental Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia)

  • Syed Masiur Rahman

    (Center for Environment and Water, Research Institute, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia)

  • Umer Mansoor

    (Civil and Environmental Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia)

  • Nedal Ratrout

    (Civil and Environmental Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia)

Abstract

Predicting crash injury severity is a crucial constituent of reducing the consequences of traffic crashes. This study developed machine learning (ML) models to predict crash injury severity using 15 crash-related parameters. Separate ML models for each cluster were obtained using fuzzy c-means, which enhanced the predicting capability. Finally, four ML models were developed: feed-forward neural networks (FNN), support vector machine (SVM), fuzzy C-means clustering based feed-forward neural network (FNN-FCM), and fuzzy c-means based support vector machine (SVM-FCM). Features that were easily identified with little investigation on crash sites were used as an input so that the trauma center can predict the crash severity level based on the initial information provided from the crash site and prepare accordingly for the treatment of the victims. The input parameters mainly include vehicle attributes and road condition attributes. This study used the crash database of Great Britain for the years 2011–2016. A random sample of crashes representing each year was used considering the same share of severe and non-severe crashes. The models were compared based on injury severity prediction accuracy, sensitivity, precision, and harmonic mean of sensitivity and precision (i.e., F1 score). The SVM-FCM model outperformed the other developed models in terms of accuracy and F1 score in predicting the injury severity level of severe and non-severe crashes. This study concluded that the FCM clustering algorithm enhanced the prediction power of FNN and SVM models.

Suggested Citation

  • Khaled Assi & Syed Masiur Rahman & Umer Mansoor & Nedal Ratrout, 2020. "Predicting Crash Injury Severity with Machine Learning Algorithm Synergized with Clustering Technique: A Promising Protocol," IJERPH, MDPI, vol. 17(15), pages 1-17, July.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:15:p:5497-:d:391899
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    References listed on IDEAS

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    1. Fang Zong & Hongguo Xu & Huiyong Zhang, 2013. "Prediction for Traffic Accident Severity: Comparing the Bayesian Network and Regression Models," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-9, October.
    2. Shengdi Chen & Shiwen Zhang & Yingying Xing & Jian Lu, 2020. "Identifying the Factors Contributing to the Severity of Truck-Involved Crashes in Shanghai River-Crossing Tunnel," IJERPH, MDPI, vol. 17(9), pages 1-15, May.
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    Cited by:

    1. Khaled Assi, 2020. "Traffic Crash Severity Prediction—A Synergy by Hybrid Principal Component Analysis and Machine Learning Models," IJERPH, MDPI, vol. 17(20), pages 1-16, October.
    2. Sheng Dong & Afaq Khattak & Irfan Ullah & Jibiao Zhou & Arshad Hussain, 2022. "Predicting and Analyzing Road Traffic Injury Severity Using Boosting-Based Ensemble Learning Models with SHAPley Additive exPlanations," IJERPH, MDPI, vol. 19(5), pages 1-23, March.
    3. Ke Zhang & Yaming Guo, 2023. "Attention-Based Residual Dilated Network for Traffic Accident Prediction," Mathematics, MDPI, vol. 11(9), pages 1-15, April.
    4. Syed As-Sadeq Tahfim & Yan Chen, 2022. "A Cluster-Based Approach for Analysis of Injury Severity in Interstate Crashes Involving Large Trucks," Sustainability, MDPI, vol. 14(21), pages 1-18, November.
    5. Wachiranun Sirikul & Nida Buawangpong & Ratana Sapbamrer & Penprapa Siviroj, 2021. "Mortality-Risk Prediction Model from Road-Traffic Injury in Drunk Drivers: Machine Learning Approach," IJERPH, MDPI, vol. 18(19), pages 1-14, October.
    6. Mireille Megnidio-Tchoukouegno & Jacob Adedayo Adedeji, 2023. "Machine Learning for Road Traffic Accident Improvement and Environmental Resource Management in the Transportation Sector," Sustainability, MDPI, vol. 15(3), pages 1-19, January.
    7. Sherif Shokry & Naglaa K. Rashwan & Seham Hemdan & Ali Alrashidi & Amr M. Wahaballa, 2023. "Characterization of Traffic Accidents Based on Long-Horizon Aggregated and Disaggregated Data," Sustainability, MDPI, vol. 15(2), pages 1-18, January.

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