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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
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    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.
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