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Open AccessArticle

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

1
National Council for Scientific Research (CNRS), Beirut 11-8281, Lebanon
2
Faculty of Engineering and Architecture, American University of Beirut, Beirut 1072020, Lebanon
3
Faculty of Health Sciences, American University of Beirut, Beirut 1072020, Lebanon
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(11), 4111; https://doi.org/10.3390/ijerph17114111
Received: 17 April 2020 / Revised: 4 June 2020 / Accepted: 5 June 2020 / Published: 9 June 2020
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. View Full-Text
Keywords: fatal crashes; road fatality factors; machine learning; classifier ensemble fatal crashes; road fatality factors; machine learning; classifier ensemble
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MDPI and ACS Style

Ghandour, A.J.; Hammoud, H.; Al-Hajj, S. Analyzing Factors Associated with Fatal Road Crashes: A Machine Learning Approach. Int. J. Environ. Res. Public Health 2020, 17, 4111. https://doi.org/10.3390/ijerph17114111

AMA Style

Ghandour AJ, Hammoud H, Al-Hajj S. Analyzing Factors Associated with Fatal Road Crashes: A Machine Learning Approach. International Journal of Environmental Research and Public Health. 2020; 17(11):4111. https://doi.org/10.3390/ijerph17114111

Chicago/Turabian Style

Ghandour, Ali J.; Hammoud, Huda; Al-Hajj, Samar. 2020. "Analyzing Factors Associated with Fatal Road Crashes: A Machine Learning Approach" Int. J. Environ. Res. Public Health 17, no. 11: 4111. https://doi.org/10.3390/ijerph17114111

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