IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i11p4111-d369177.html
   My bibliography  Save this article

Analyzing Factors Associated with Fatal Road Crashes: A Machine Learning Approach

Author

Listed:
  • Ali J. Ghandour

    (National Council for Scientific Research (CNRS), Beirut 11-8281, Lebanon)

  • Huda Hammoud

    (Faculty of Engineering and Architecture, American University of Beirut, Beirut 1072020, Lebanon)

  • Samar Al-Hajj

    (Faculty of Health Sciences, American University of Beirut, Beirut 1072020, Lebanon)

Abstract

Road traffic injury accounts for a substantial human and economic burden globally. Understanding risk factors contributing to fatal injuries is of paramount importance. In this study, we proposed a model that adopts a hybrid ensemble machine learning classifier structured from sequential minimal optimization and decision trees to identify risk factors contributing to fatal road injuries. The model was constructed, trained, tested, and validated using the Lebanese Road Accidents Platform (LRAP) database of 8482 road crash incidents, with fatality occurrence as the outcome variable. A sensitivity analysis was conducted to examine the influence of multiple factors on fatality occurrence. Seven out of the nine selected independent variables were significantly associated with fatality occurrence, namely, crash type, injury severity, spatial cluster-ID, and crash time (hour). Evidence gained from the model data analysis will be adopted by policymakers and key stakeholders to gain insights into major contributing factors associated with fatal road crashes and to translate knowledge into safety programs and enhanced road policies.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:11:p:4111-:d:369177
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/11/4111/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/17/11/4111/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ghandour, Ali J. & Lovallo, Michele & Telesca, Luciano, 2019. "Time-clustering behavior and cycles in the time dynamics of car accident sequences in Lebanon," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 178-184.
    2. 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.
    3. Ghandour, Ali J. & Hammoud, Huda & Telesca, Luciano, 2019. "Transportation hazard spatial analysis using crowd-sourced social network data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 309-316.
    Full references (including those not matched with items on IDEAS)

    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. Ghandour, Ali J. & Hammoud, Huda & Dimassi, Mohammad & Krayem, Houssam & Haydar, Jamal & Issa, Adam, 2020. "Allometric scaling of road accidents using social media crowd-sourced data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    2. 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.
    3. Jie-Huei Wang & Cheng-Yu Liu & You-Ruei Min & Zih-Han Wu & Po-Lin Hou, 2024. "Cancer Diagnosis by Gene-Environment Interactions via Combination of SMOTE-Tomek and Overlapped Group Screening Approaches with Application to Imbalanced TCGA Clinical and Genomic Data," Mathematics, MDPI, vol. 12(14), pages 1-24, July.
    4. 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.
    5. 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.
    6. 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.
    7. Fan, Xudong & Wang, Xiaowei & Zhang, Xijin & ASCE Xiong (Bill) Yu, P.E.F., 2022. "Machine learning based water pipe failure prediction: The effects of engineering, geology, climate and socio-economic factors," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    8. Zhang, Han, 2021. "How Using Machine Learning Classification as a Variable in Regression Leads to Attenuation Bias and What to Do About It," SocArXiv 453jk, Center for Open Science.
    9. 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.
    10. Daniel R Jeske, 2018. "Metrics Used When Evaluating the Performance of Statistical Classifiers," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 8(1), pages 7-9, August.
    11. Juliet Chebet Moso & Stéphane Cormier & Cyril de Runz & Hacène Fouchal & John Mwangi Wandeto, 2021. "Anomaly Detection on Data Streams for Smart Agriculture," Agriculture, MDPI, vol. 11(11), pages 1-17, November.
    12. 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.
    13. 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.
    14. Robert A. Blair & Nicholas Sambanis, 2021. "Is Theory Useful for Conflict Prediction? A Response to Beger, Morgan, and Ward," Journal of Conflict Resolution, Peace Science Society (International), vol. 65(7-8), pages 1427-1453, August.
    15. Mieke Deschepper & Willem Waegeman & Dirk Vogelaers & Kristof Eeckloo, 2020. "Using structured pathology data to predict hospital-wide mortality at admission," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-11, June.
    16. Hala Aburas & Isam Shahrour, 2021. "Impact of the Mobility Restrictions in the Palestinian Territory on the Population and the Environment," Sustainability, MDPI, vol. 13(23), pages 1-14, December.
    17. 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.
    18. Falco J. Bargagli-Dtoffi & Massimo Riccaboni & Armando Rungi, 2020. "Machine Learning for Zombie Hunting. Firms Failures and Financial Constraints," Working Papers 01/2020, IMT School for Advanced Studies Lucca, revised Jun 2020.
    19. 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.
    20. Jianyuan Deng & Zhibo Yang & Hehe Wang & Iwao Ojima & Dimitris Samaras & Fusheng Wang, 2023. "A systematic study of key elements underlying molecular property prediction," Nature Communications, Nature, vol. 14(1), pages 1-20, December.

    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:jijerp:v:17:y:2020:i:11:p:4111-:d:369177. 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.