IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i7p4101-d783314.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/7/4101/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/7/4101/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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).
    4. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Christopher J Greenwood & George J Youssef & Primrose Letcher & Jacqui A Macdonald & Lauryn J Hagg & Ann Sanson & Jenn Mcintosh & Delyse M Hutchinson & John W Toumbourou & Matthew Fuller-Tyszkiewicz &, 2020. "A comparison of penalised regression methods for informing the selection of predictive markers," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-14, November.
    2. Le, Hong Hanh & Viviani, Jean-Laurent, 2018. "Predicting bank failure: An improvement by implementing a machine-learning approach to classical financial ratios," Research in International Business and Finance, Elsevier, vol. 44(C), pages 16-25.
    3. João Chang Junior & Fábio Binuesa & Luiz Fernando Caneo & Aida Luiza Ribeiro Turquetto & Elisandra Cristina Trevisan Calvo Arita & Aline Cristina Barbosa & Alfredo Manoel da Silva Fernandes & Evelinda, 2020. "Improving preoperative risk-of-death prediction in surgery congenital heart defects using artificial intelligence model: A pilot study," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-21, September.
    4. Arthur De Sá Ferreira & Ney Meziat-Filho & Ana Paula Antunes Ferreira, 2021. "Double threshold receiver operating characteristic plot for three-modal continuous predictors," Computational Statistics, Springer, vol. 36(3), pages 2231-2245, September.
    5. Masabho P Milali & Samson S Kiware & Nicodem J Govella & Fredros Okumu & Naveen Bansal & Serdar Bozdag & Jacques D Charlwood & Marta F Maia & Sheila B Ogoma & Floyd E Dowell & George F Corliss & Maggy, 2020. "An autoencoder and artificial neural network-based method to estimate parity status of wild mosquitoes from near-infrared spectra," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-16, June.
    6. Kajal Lahiri & Cheng Yang, 2023. "ROC and PRC Approaches to Evaluate Recession Forecasts," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 19(2), pages 119-148, September.
    7. Tzu-Hsuan Lin & Jehn-Ruey Jiang, 2021. "Credit Card Fraud Detection with Autoencoder and Probabilistic Random Forest," Mathematics, MDPI, vol. 9(21), pages 1-16, October.
    8. Alfred Krzywicki & David Muchlinski & Benjamin E. Goldsmith & Arcot Sowmya, 2022. "From academia to policy makers: a methodology for real-time forecasting of infrequent events," Journal of Computational Social Science, Springer, vol. 5(2), pages 1489-1510, November.
    9. Marco Due~nas & V'ictor Ortiz & Massimo Riccaboni & Francesco Serti, 2021. "Assessing the Impact of COVID-19 on Trade: a Machine Learning Counterfactual Analysis," Papers 2104.04570, arXiv.org.
    10. Wei-Hsuan Lo-Ciganic & Julie M Donohue & Eric G Hulsey & Susan Barnes & Yuan Li & Courtney C Kuza & Qingnan Yang & Jeanine Buchanich & James L Huang & Christina Mair & Debbie L Wilson & Walid F Gellad, 2021. "Integrating human services and criminal justice data with claims data to predict risk of opioid overdose among Medicaid beneficiaries: A machine-learning approach," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-18, March.
    11. Nica-Avram, Georgiana & Harvey, John & Smith, Gavin & Smith, Andrew & Goulding, James, 2021. "Identifying food insecurity in food sharing networks via machine learning," Journal of Business Research, Elsevier, vol. 131(C), pages 469-484.
    12. Ali J. Ghandour & Huda Hammoud & Samar Al-Hajj, 2020. "Analyzing Factors Associated with Fatal Road Crashes: A Machine Learning Approach," IJERPH, MDPI, vol. 17(11), pages 1-13, June.
    13. Song, Kwonsik & Anderson, Kyle & Lee, SangHyun, 2020. "An energy-cyber-physical system for personalized normative messaging interventions: Identification and classification of behavioral reference groups," Applied Energy, Elsevier, vol. 260(C).
    14. Fisnik Doko & Slobodan Kalajdziski & Igor Mishkovski, 2021. "Credit Risk Model Based on Central Bank Credit Registry Data," JRFM, MDPI, vol. 14(3), pages 1-17, March.
    15. Abouelmagd THM, 2018. "A New Flexible Distribution Based on the Zero Truncated Poisson Distribution: Mathematical Properties and Applications to Lifetime Data," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 8(1), pages 10-16, August.
    16. Bouvatier, Vincent & El Ouardi, Sofiane, 2023. "Credit gaps as banking crisis predictors: A different tune for middle- and low-income countries," Emerging Markets Review, Elsevier, vol. 54(C).
    17. Faith M. Hartley & Aaron E. Maxwell & Rick E. Landenberger & Zachary J. Bortolot, 2022. "Forest Type Differentiation Using GLAD Phenology Metrics, Land Surface Parameters, and Machine Learning," Geographies, MDPI, vol. 2(3), pages 1-25, August.
    18. Artur Sokolovsky & Luca Arnaboldi & Jaume Bacardit & Thomas Gross, 2021. "Volume-Centred Range Bars: Novel Interpretable Representation of Financial Markets Designed for Machine Learning Applications," Papers 2103.12419, arXiv.org, revised May 2022.
    19. Soyoung Oh & Honggeun Ji & Jina Kim & Eunil Park & Angel P. del Pobil, 2022. "Deep learning model based on expectation-confirmation theory to predict customer satisfaction in hospitality service," Information Technology & Tourism, Springer, vol. 24(1), pages 109-126, March.
    20. John Muschelli, 2020. "ROC and AUC with a Binary Predictor: a Potentially Misleading Metric," Journal of Classification, Springer;The Classification Society, vol. 37(3), pages 696-708, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:7:p:4101-:d:783314. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.