Severity Analysis of Heavy Vehicle Crashes Using Machine Learning Models: A Case Study in New Jersey
Publication: International Conference on Transportation and Development 2021
ABSTRACT
Large trucks are a vital mode for freight transportation. Increasing demand in freight transportation increases the risk of truck-involved crashes on highways. Truck-involved crashes counted for 4,761 deaths in the United States during the year 2017, which is 12% more than the death toll of the year 2008. We gathered four years (2016–2019) of motor vehicle crashes involving large trucks in New Jersey for further analysis. The data set is classified into three injury severity categories, including severe injury, possible injury, and no injury. To predict the crash severity, we trained and tested the data set with the three most commonly used machine learning models, including support vector machine, random forest, and boosting methods. The performance of the models is evaluated by their precision, accuracy, and recall. Further, a sensitivity analysis was performed to demonstrate the impact of the top contributing factors in truck-related crashes. The findings of the study will help practitioners and policymakers determine the effects of influential parameters on injury severity outcomes and take necessary countermeasures to minimize the frequency and severity of large-truck crashes.
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International Conference on Transportation and Development 2021
Pages: 285 - 296
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© 2021 American Society of Civil Engineers.
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Published online: Jun 4, 2021
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