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Crash Severity Analysis of Highways Based on Multinomial Logistic Regression Model, Decision Tree Techniques, and Artificial Neural Network: A Modeling Comparison

Author

Listed:
  • Gholamreza Shiran

    (Faculty of Civil Engineering and Transportation, University of Isfahan, Isfahan 8174673441, Iran)

  • Reza Imaninasab

    (Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA)

  • Razieh Khayamim

    (Department of Transportation Engineering, Isfahan University of Technology, Isfahan 8415683111, Iran)

Abstract

The classification of vehicular crashes based on their severity is crucial since not all of them have the same financial and injury values. In addition, avoiding crashes by identifying their influential factors is possible via accurate prediction modeling. In crash severity analysis, accurate and time-saving prediction models are necessary for classifying crashes based on their severity. Moreover, statistical models are incapable of identifying the potential severity of crashes regarding influencing factors incorporated in models. Unlike previous research efforts, which focused on the limited class of crash severity, including property damage only (PDO), fatality, and injury by applying data mining models, the present study sought to predict crash frequency according to five severity levels of PDO, fatality, severe injury, other visible injuries, and complaint of pain. The multinomial logistic regression (MLR) model and data mining approaches, including artificial neural network-multilayer perceptron (ANN-MLP) and two decision tree techniques, (i.e., Chi-square automatic interaction detector (CHAID) and C5.0) are utilized based on traffic crash records for State Highways in California, USA. The comparison of the findings of the relative importance of ten qualitative and ten quantitative independent variables incorporated in CHAID and C5.0 indicated that the cause of the crash (X 1 ) and the number of vehicles (X 5 ) were known as the most influential variables involved in the crash. However, the cause of the crash (X 1 ) and weather (X 2 ) were identified as the most contributing variables by the ANN-MLP model. In addition, the MLR model showed that the driver’s age (X 11 ) accounts for a larger proportion of traffic crash severity. Therefore, the sensitivity analysis demonstrated that C5.0 had the best performance for predicting road crash severity. Not only did C5.0 take a shorter time (0.05 s) compared to CHAID, MLP, and MLR, it also represented the highest accuracy rate for the training set. The overall prediction accuracy based on the training data was approximately 88.09% compared to 77.21% and 70.21% for CHAID and MLP models. In general, the findings of this study revealed that C5.0 can be a promising tool for predicting road crash severity.

Suggested Citation

  • Gholamreza Shiran & Reza Imaninasab & Razieh Khayamim, 2021. "Crash Severity Analysis of Highways Based on Multinomial Logistic Regression Model, Decision Tree Techniques, and Artificial Neural Network: A Modeling Comparison," Sustainability, MDPI, vol. 13(10), pages 1-23, May.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:10:p:5670-:d:557267
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    References listed on IDEAS

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    1. Milanović Marina & Stamenković Milan, 2016. "CHAID Decision Tree: Methodological Frame and Application," Economic Themes, Sciendo, vol. 54(4), pages 563-586, December.
    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.
    3. G. V. Kass, 1980. "An Exploratory Technique for Investigating Large Quantities of Categorical Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(2), pages 119-127, June.
    4. Song Wang & Zhixia Li, 2019. "Exploring the mechanism of crashes with automated vehicles using statistical modeling approaches," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-16, March.
    5. Fang Zong & Huiyong Zhang & Hongguo Xu & Xiumei Zhu & Lu Wang, 2013. "Predicting Severity and Duration of Road Traffic Accident," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-9, December.
    6. Hamid Behbahani & Amir Mohamadian Amiri & Reza Imaninasab & Meysam Alizamir, 2018. "Forecasting accident frequency of an urban road network: A comparison of four artificial neural network techniques," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(7), pages 767-780, November.
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    Cited by:

    1. Fu Wang & Jing Wang & Xianfeng Zhang & Dengjun Gu & Yang Yang & Hongbin Zhu, 2022. "Analysis of the Causes of Traffic Accidents and Identification of Accident-Prone Points in Long Downhill Tunnel of Mountain Expressways Based on Data Mining," Sustainability, MDPI, vol. 14(14), pages 1-22, July.
    2. Alicja Wolny-Dominiak & Tomasz Żądło, 2021. "The Measures of Accuracy of Claim Frequency Credibility Predictor," Sustainability, MDPI, vol. 13(21), pages 1-13, October.
    3. Mubarak Alrumaidhi & Hesham A. Rakha, 2022. "Factors Affecting Crash Severity among Elderly Drivers: A Multilevel Ordinal Logistic Regression Approach," Sustainability, MDPI, vol. 14(18), pages 1-12, September.
    4. Miaomiao Yan & Yindong Shen, 2022. "Traffic Accident Severity Prediction Based on Random Forest," Sustainability, MDPI, vol. 14(3), pages 1-13, February.
    5. Debela Jima & Tibor Sipos, 2022. "The Impact of Road Geometric Formation on Traffic Crash and Its Severity Level," Sustainability, MDPI, vol. 14(14), pages 1-25, July.

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