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A comparative study on machine learning based algorithms for prediction of motorcycle crash severity

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  • Lukuman Wahab
  • Haobin Jiang

Abstract

Motorcycle crash severity is under-researched in Ghana. Thus, the probable risk factors and association between these factors and motorcycle crash severity outcomes is not known. Traditional statistical models have intrinsic assumptions and pre-defined correlations that, if flouted, can generate inaccurate results. In this study, machine learning based algorithms were employed to predict and classify motorcycle crash severity. Machine learning based techniques are non-parametric models without the presumption of relationships between endogenous and exogenous variables. The main aim of this research is to evaluate and compare different approaches to modeling motorcycle crash severity as well as investigating the effect of risk factors on the injury outcomes of motorcycle crashes. Motorcycle crash dataset between 2011 and 2015 was extracted from the National Road Traffic Crash Database at the Building and Road Research Institute (BRRI) in Ghana. The dataset was classified into four injury severity categories: fatal, hospitalized, injured, and damage-only. Three machine learning based models were developed: J48 Decision Tree Classifier, Random Forest (RF) and Instance-Based learning with parameter k (IBk) were employed to model the severity of injury in a motorcycle crash. These machine learning algorithms were validated using 10-fold cross-validation technique. The three machine learning based algorithms were compared with one another and the statistical model: multinomial logit model (MNLM). Also, the relative importance analysis of the attribute was conducted to determine the impact of these attributes on injury severity outcomes. The results of the study reveal that the predictions of machine learning algorithms are superior to the MNLM in accuracy and effectiveness, and the RF-based algorithms show the overall best agreement with the experimental data out of the three machine learning algorithms, for its global optimization and extrapolation ability. Location type, time of the crash, settlement type, collision partner, collision type, road separation, road surface type, the day of the week, and road shoulder condition were found as the critical determinants of motorcycle crash injury severity.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0214966
    DOI: 10.1371/journal.pone.0214966
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    References listed on IDEAS

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    1. Yajie Zou & John E. Ash & Byung-Jung Park & Dominique Lord & Lingtao Wu, 2018. "Empirical Bayes estimates of finite mixture of negative binomial regression models and its application to highway safety," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(9), pages 1652-1669, July.
    2. Xin Ye & Ke Wang & Yajie Zou & Dominique Lord, 2018. "A semi-nonparametric Poisson regression model for analyzing motor vehicle crash data," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-17, May.
    3. Yanyong Guo & Jibiao Zhou & Yao Wu & Zhibin Li, 2017. "Identifying the factors affecting bike-sharing usage and degree of satisfaction in Ningbo, China," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-19, September.
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    Cited by:

    1. Mansoor, Umer & Jamal, Arshad & Su, Junbiao & Sze, N.N. & Chen, Anthony, 2023. "Investigating the risk factors of motorcycle crash injury severity in Pakistan: Insights and policy recommendations," Transport Policy, Elsevier, vol. 139(C), pages 21-38.
    2. Mohammed Abdul Muhsin Zambang & Haobin Jiang & Lukuman Wahab, 2021. "Modeling vehicle ownership with machine learning techniques in the Greater Tamale Area, Ghana," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-18, February.
    3. 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.
    4. 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.

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